1. Table of Contents


Description

1.1 Introduction

1.1.1 Study Objectives

1.1.2 Outcome

1.1.3 Predictors

1.2 Methodology

1.2.1 Model Formulation

1.2.2 Model Evaluation

1.2.3 Model Post-Hoc Analysis

1.3 Results

1.3.1 Data Preparation


Code Chunk | Output
##################################
# Loading R libraries
##################################
library(DALEX)
library(caret)
library(randomForest)
library(e1071)
library(gbm)
library(skimr)
library(corrplot)
library(lares)
library(dplyr)
library(minerva)
library(CORElearn)
library(patchwork)

##################################
# Loading source and
# formulating the analysis set
##################################
LED <- read.csv("Life_Expectancy_Data.csv",
                na.strings=c("NA","NaN"," ",""),
                stringsAsFactors = FALSE)
LED <- as.data.frame(LED)

##################################
# Performing a general exploration of the data set
##################################
dim(LED)
## [1] 396  22
str(LED)
## 'data.frame':    396 obs. of  22 variables:
##  $ COUNTRY: chr  "Afghanistan" "Albania" "Algeria" "Angola" ...
##  $ YEAR   : int  2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 ...
##  $ GENDER : chr  "Female" "Female" "Female" "Female" ...
##  $ LIFEXP : num  66.4 80.2 78.1 64 78.1 ...
##  $ UNEMPR : num  14.06 11.32 18.63 7.84 8.26 ...
##  $ INFMOR : num  42.9 7.7 18.6 44.5 5.1 ...
##  $ GDP    : num  1.88e+10 1.54e+10 1.72e+11 8.94e+10 1.69e+09 ...
##  $ GNI    : num  1.91e+10 1.52e+10 1.68e+11 8.19e+10 1.58e+09 ...
##  $ CLTECH : num  36 80.7 99.3 49.6 100 ...
##  $ PERCAP : num  494 5396 3990 2810 17377 ...
##  $ RTIMOR : num  15.9 11.7 20.9 26.1 0 ...
##  $ TUBINC : num  189 16 61 351 0 29 26 2.2 6.9 6 ...
##  $ DPTIMM : num  66 99 91 57 95 ...
##  $ HEPIMM : num  66 99 91 53 99 ...
##  $ MEAIMM : num  64 95 80 51 93 ...
##  $ HOSBED : num  0.432 3.052 1.8 0.8 2.581 ...
##  $ SANSER : num  49 99.2 86.1 51.4 85.5 ...
##  $ TUBTRT : num  91 88 86 69 72.3 ...
##  $ URBPOP : num  25.8 61.2 73.2 66.2 24.5 ...
##  $ RURPOP : num  74.2 38.8 26.8 33.8 75.5 ...
##  $ NCOMOR : num  36.2 6 12.8 19.4 17.6 ...
##  $ SUIRAT : num  3.6 2.7 1.8 2.3 0.8 ...
summary(LED)
##    COUNTRY               YEAR         GENDER              LIFEXP     
##  Length:396         Min.   :2019   Length:396         Min.   :51.20  
##  Class :character   1st Qu.:2019   Class :character   1st Qu.:67.62  
##  Mode  :character   Median :2019   Mode  :character   Median :74.38  
##                     Mean   :2019                      Mean   :73.10  
##                     3rd Qu.:2019                      3rd Qu.:79.31  
##                     Max.   :2019                      Max.   :88.10  
##      UNEMPR           INFMOR           GDP                 GNI           
##  Min.   : 0.071   Min.   : 1.40   Min.   :1.884e+08   Min.   :3.754e+08  
##  1st Qu.: 3.575   1st Qu.: 5.90   1st Qu.:1.131e+10   1st Qu.:1.114e+10  
##  Median : 5.647   Median :15.05   Median :3.916e+10   Median :4.007e+10  
##  Mean   : 7.749   Mean   :21.46   Mean   :5.762e+11   Mean   :5.972e+11  
##  3rd Qu.: 9.839   3rd Qu.:30.38   3rd Qu.:2.500e+11   3rd Qu.:2.460e+11  
##  Max.   :41.153   Max.   :88.80   Max.   :2.320e+13   Max.   :2.340e+13  
##      CLTECH           PERCAP             RTIMOR          TUBINC   
##  Min.   :  0.00   Min.   :   228.2   Min.   : 0.00   Min.   :  0  
##  1st Qu.: 33.50   1st Qu.:  2229.9   1st Qu.: 8.20   1st Qu.: 12  
##  Median : 80.10   Median :  6617.1   Median :15.95   Median : 46  
##  Mean   : 65.83   Mean   : 16917.8   Mean   :16.98   Mean   :103  
##  3rd Qu.:100.00   3rd Qu.: 19575.8   3rd Qu.:23.90   3rd Qu.:140  
##  Max.   :100.00   Max.   :175813.9   Max.   :64.60   Max.   :654  
##      DPTIMM          HEPIMM          MEAIMM          HOSBED      
##  Min.   :35.00   Min.   :35.00   Min.   :37.00   Min.   : 0.200  
##  1st Qu.:85.69   1st Qu.:81.31   1st Qu.:84.85   1st Qu.: 1.300  
##  Median :92.00   Median :91.00   Median :92.00   Median : 2.572  
##  Mean   :87.90   Mean   :86.65   Mean   :87.22   Mean   : 2.987  
##  3rd Qu.:97.00   3rd Qu.:96.00   3rd Qu.:96.00   3rd Qu.: 3.746  
##  Max.   :99.00   Max.   :99.00   Max.   :99.00   Max.   :13.710  
##      SANSER            TUBTRT           URBPOP           RURPOP     
##  Min.   :  8.632   Min.   :  0.00   Min.   : 13.25   Min.   : 0.00  
##  1st Qu.: 63.898   1st Qu.: 73.00   1st Qu.: 41.61   1st Qu.:21.90  
##  Median : 91.239   Median : 82.00   Median : 58.90   Median :41.10  
##  Mean   : 77.606   Mean   : 77.66   Mean   : 59.21   Mean   :40.79  
##  3rd Qu.: 98.648   3rd Qu.: 88.00   3rd Qu.: 78.10   3rd Qu.:58.39  
##  Max.   :100.000   Max.   :100.00   Max.   :100.00   Max.   :86.75  
##      NCOMOR          SUIRAT     
##  Min.   : 4.40   Min.   : 0.00  
##  1st Qu.:13.60   1st Qu.: 3.30  
##  Median :19.85   Median : 6.95  
##  Mean   :19.99   Mean   : 9.34  
##  3rd Qu.:24.02   3rd Qu.:11.22  
##  Max.   :58.40   Max.   :63.00
##################################
# Transforming to appropriate data types
##################################
LED$YEAR <- factor(LED$YEAR,
                      levels = c("2019"))
LED$GENDER <- factor(LED$GENDER,
                      levels = c("Male","Female"))

##################################
# Reducing the range of values
# for certain numeric predictors
##################################
LED$GDP     <- LED$GDP/1000000000
LED$GNI     <- LED$GNI/1000000000
LED$PERCAP  <- LED$PERCAP/1000

##################################
# Formulating a data type assessment summary
##################################
PDA <- LED
(PDA.Summary <- data.frame(
  Column.Index=c(1:length(names(PDA))),
  Column.Name= names(PDA), 
  Column.Type=sapply(PDA, function(x) class(x)), 
  row.names=NULL)
)
##    Column.Index Column.Name Column.Type
## 1             1     COUNTRY   character
## 2             2        YEAR      factor
## 3             3      GENDER      factor
## 4             4      LIFEXP     numeric
## 5             5      UNEMPR     numeric
## 6             6      INFMOR     numeric
## 7             7         GDP     numeric
## 8             8         GNI     numeric
## 9             9      CLTECH     numeric
## 10           10      PERCAP     numeric
## 11           11      RTIMOR     numeric
## 12           12      TUBINC     numeric
## 13           13      DPTIMM     numeric
## 14           14      HEPIMM     numeric
## 15           15      MEAIMM     numeric
## 16           16      HOSBED     numeric
## 17           17      SANSER     numeric
## 18           18      TUBTRT     numeric
## 19           19      URBPOP     numeric
## 20           20      RURPOP     numeric
## 21           21      NCOMOR     numeric
## 22           22      SUIRAT     numeric

1.3.2 Data Quality Assessment


Code Chunk | Output
##################################
# Loading dataset
##################################
DQA <- LED

##################################
# Formulating an overall data quality assessment summary
##################################
(DQA.Summary <- data.frame(
  Column.Index=c(1:length(names(DQA))),
  Column.Name= names(DQA),
  Column.Type=sapply(DQA, function(x) class(x)),
  Row.Count=sapply(DQA, function(x) nrow(DQA)),
  NA.Count=sapply(DQA,function(x)sum(is.na(x))),
  Fill.Rate=sapply(DQA,function(x)format(round((sum(!is.na(x))/nrow(DQA)),3),nsmall=3)),
  row.names=NULL)
)
##    Column.Index Column.Name Column.Type Row.Count NA.Count Fill.Rate
## 1             1     COUNTRY   character       396        0     1.000
## 2             2        YEAR      factor       396        0     1.000
## 3             3      GENDER      factor       396        0     1.000
## 4             4      LIFEXP     numeric       396        0     1.000
## 5             5      UNEMPR     numeric       396        0     1.000
## 6             6      INFMOR     numeric       396        0     1.000
## 7             7         GDP     numeric       396        0     1.000
## 8             8         GNI     numeric       396        0     1.000
## 9             9      CLTECH     numeric       396        0     1.000
## 10           10      PERCAP     numeric       396        0     1.000
## 11           11      RTIMOR     numeric       396        0     1.000
## 12           12      TUBINC     numeric       396        0     1.000
## 13           13      DPTIMM     numeric       396        0     1.000
## 14           14      HEPIMM     numeric       396        0     1.000
## 15           15      MEAIMM     numeric       396        0     1.000
## 16           16      HOSBED     numeric       396        0     1.000
## 17           17      SANSER     numeric       396        0     1.000
## 18           18      TUBTRT     numeric       396        0     1.000
## 19           19      URBPOP     numeric       396        0     1.000
## 20           20      RURPOP     numeric       396        0     1.000
## 21           21      NCOMOR     numeric       396        0     1.000
## 22           22      SUIRAT     numeric       396        0     1.000
##################################
# Listing all Predictors
##################################
DQA.Predictors <- DQA[,!names(DQA) %in% c("COUNTRY","YEAR","LIFEXP")]

##################################
# Listing all numeric Predictors
##################################
DQA.Predictors.Numeric <- DQA.Predictors[,sapply(DQA.Predictors, is.numeric), drop = FALSE]

if (length(names(DQA.Predictors.Numeric))>0) {
    print(paste0("There is (are) ",
               (length(names(DQA.Predictors.Numeric))),
               " numeric descriptor variable(s)."))
} else {
  print("There are no numeric descriptor variables.")
}
## [1] "There is (are) 18 numeric descriptor variable(s)."
##################################
# Listing all factor Predictors
##################################
DQA.Predictors.Factor <- DQA.Predictors[,sapply(DQA.Predictors, is.factor), drop = FALSE]

if (length(names(DQA.Predictors.Factor))>0) {
    print(paste0("There is (are) ",
               (length(names(DQA.Predictors.Factor))),
               " factor descriptor variable(s)."))
} else {
  print("There are no factor descriptor variables.")
}
## [1] "There is (are) 1 factor descriptor variable(s)."
##################################
# Formulating a data quality assessment summary for factor Predictors
##################################
if (length(names(DQA.Predictors.Factor))>0) {

  ##################################
  # Formulating a function to determine the first mode
  ##################################
  FirstModes <- function(x) {
    ux <- unique(na.omit(x))
    tab <- tabulate(match(x, ux))
    ux[tab == max(tab)]
  }

  ##################################
  # Formulating a function to determine the second mode
  ##################################
  SecondModes <- function(x) {
    ux <- unique(na.omit(x))
    tab <- tabulate(match(x, ux))
    fm = ux[tab == max(tab)]
    sm = x[!(x %in% fm)]
    usm <- unique(sm)
    tabsm <- tabulate(match(sm, usm))
    ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
           return("x"),
           return(usm[tabsm == max(tabsm)]))
  }

  (DQA.Predictors.Factor.Summary <- data.frame(
  Column.Name= names(DQA.Predictors.Factor),
  Column.Type=sapply(DQA.Predictors.Factor, function(x) class(x)),
  Unique.Count=sapply(DQA.Predictors.Factor, function(x) length(unique(x))),
  First.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(FirstModes(x)[1])),
  Second.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(SecondModes(x)[1])),
  First.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == FirstModes(x)[1])),
  Second.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == SecondModes(x)[1])),
  Unique.Count.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Factor)),3), nsmall=3)),
  First.Second.Mode.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
  row.names=NULL)
  )

}
##   Column.Name Column.Type Unique.Count First.Mode.Value Second.Mode.Value
## 1      GENDER      factor            2           Female                 x
##   First.Mode.Count Second.Mode.Count Unique.Count.Ratio First.Second.Mode.Ratio
## 1              198                 0              0.005                     Inf
##################################
# Formulating a data quality assessment summary for numeric Predictors
##################################
if (length(names(DQA.Predictors.Numeric))>0) {

  ##################################
  # Formulating a function to determine the first mode
  ##################################
  FirstModes <- function(x) {
    ux <- unique(na.omit(x))
    tab <- tabulate(match(x, ux))
    ux[tab == max(tab)]
  }

  ##################################
  # Formulating a function to determine the second mode
  ##################################
  SecondModes <- function(x) {
    ux <- unique(na.omit(x))
    tab <- tabulate(match(x, ux))
    fm = ux[tab == max(tab)]
    sm = na.omit(x)[!(na.omit(x) %in% fm)]
    usm <- unique(sm)
    tabsm <- tabulate(match(sm, usm))
    ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
           return(0.00001),
           return(usm[tabsm == max(tabsm)]))
  }

  (DQA.Predictors.Numeric.Summary <- data.frame(
  Column.Name= names(DQA.Predictors.Numeric),
  Column.Type=sapply(DQA.Predictors.Numeric, function(x) class(x)),
  Unique.Count=sapply(DQA.Predictors.Numeric, function(x) length(unique(x))),
  Unique.Count.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Numeric)),3), nsmall=3)),
  First.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((FirstModes(x)[1]),3),nsmall=3)),
  Second.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((SecondModes(x)[1]),3),nsmall=3)),
  First.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == FirstModes(x)[1])),
  Second.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == SecondModes(x)[1])),
  First.Second.Mode.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
  Minimum=sapply(DQA.Predictors.Numeric, function(x) format(round(min(x,na.rm = TRUE),3), nsmall=3)),
  Mean=sapply(DQA.Predictors.Numeric, function(x) format(round(mean(x,na.rm = TRUE),3), nsmall=3)),
  Median=sapply(DQA.Predictors.Numeric, function(x) format(round(median(x,na.rm = TRUE),3), nsmall=3)),
  Maximum=sapply(DQA.Predictors.Numeric, function(x) format(round(max(x,na.rm = TRUE),3), nsmall=3)),
  Skewness=sapply(DQA.Predictors.Numeric, function(x) format(round(skewness(x,na.rm = TRUE),3), nsmall=3)),
  Kurtosis=sapply(DQA.Predictors.Numeric, function(x) format(round(kurtosis(x,na.rm = TRUE),3), nsmall=3)),
  Percentile25th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.25,na.rm = TRUE),3), nsmall=3)),
  Percentile75th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.75,na.rm = TRUE),3), nsmall=3)),
  row.names=NULL)
  )

}
##    Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 1       UNEMPR     numeric          372              0.939            8.256
## 2       INFMOR     numeric          245              0.619           30.235
## 3          GDP     numeric          193              0.487         1390.000
## 4          GNI     numeric          192              0.485         2040.000
## 5       CLTECH     numeric          112              0.283          100.000
## 6       PERCAP     numeric          197              0.497           12.669
## 7       RTIMOR     numeric          142              0.359           18.229
## 8       TUBINC     numeric          146              0.369          136.043
## 9       DPTIMM     numeric           46              0.116           99.000
## 10      HEPIMM     numeric           46              0.116           81.308
## 11      MEAIMM     numeric           48              0.121           99.000
## 12      HOSBED     numeric          174              0.439            2.986
## 13      SANSER     numeric          187              0.472          100.000
## 14      TUBTRT     numeric           59              0.149           84.000
## 15      URBPOP     numeric          192              0.485          100.000
## 16      RURPOP     numeric          192              0.485            0.000
## 17      NCOMOR     numeric          216              0.545           22.100
## 18      SUIRAT     numeric          177              0.447           10.619
##    Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 1              3.924               22                 2                  11.000
## 2              2.100               28                 7                   4.000
## 3             18.799                4                 2                   2.000
## 4            316.000                8                 4                   2.000
## 5             60.593              110                34                   3.235
## 6              0.494                4                 2                   2.000
## 7             26.800               28                 6                   4.667
## 8             35.000               12                10                   1.200
## 9             85.685               44                30                   1.467
## 10            99.000               40                38                   1.053
## 11            84.855               48                30                   1.600
## 12             0.400               34                 8                   4.250
## 13            49.006               24                 2                  12.000
## 14            83.000               22                20                   1.100
## 15            55.985               10                 4                   2.500
## 16            44.015               10                 4                   2.500
## 17             6.800               30                 5                   6.000
## 18             7.600               30                 8                   3.750
##    Minimum    Mean Median   Maximum Skewness Kurtosis Percentile25th
## 1    0.071   7.749  5.648    41.153    1.758    3.711          3.575
## 2    1.400  21.464 15.050    88.800    1.090    0.580          5.900
## 3    0.188 576.200 39.162 23200.000    7.506   59.379         11.315
## 4    0.375 597.197 40.068 23400.000    7.467   59.073         11.141
## 5    0.000  65.833 80.100   100.000   -0.630   -1.132         33.500
## 6    0.228  16.918  6.617   175.814    2.746   10.405          2.230
## 7    0.000  16.977 15.950    64.600    0.747    1.046          8.200
## 8    0.000 102.983 46.000   654.000    1.870    3.203         12.000
## 9   35.000  87.904 92.000    99.000   -1.864    3.470         85.685
## 10  35.000  86.654 91.000    99.000   -1.602    2.510         81.308
## 11  37.000  87.221 92.000    99.000   -1.695    2.607         84.855
## 12   0.200   2.987  2.572    13.710    1.700    3.893          1.300
## 13   8.632  77.606 91.239   100.000   -1.129   -0.139         63.898
## 14   0.000  77.662 82.000   100.000   -2.197    5.606         73.000
## 15  13.250  59.211 58.900   100.000   -0.141   -0.994         41.612
## 16   0.000  40.789 41.100    86.750    0.141   -0.994         21.901
## 17   4.400  19.986 19.850    58.400    0.870    1.558         13.600
## 18   0.000   9.340  6.950    63.000    2.325    7.134          3.300
##    Percentile75th
## 1           9.840
## 2          30.376
## 3         250.000
## 4         246.000
## 5         100.000
## 6          19.576
## 7          23.900
## 8         140.000
## 9          97.000
## 10         96.000
## 11         96.000
## 12          3.746
## 13         98.648
## 14         88.000
## 15         78.099
## 16         58.388
## 17         24.025
## 18         11.225
##################################
# Identifying potential data quality issues
##################################

##################################
# Checking for missing observations
##################################
if ((nrow(DQA.Summary[DQA.Summary$NA.Count>0,]))>0){
  print(paste0("Missing observations noted for ",
               (nrow(DQA.Summary[DQA.Summary$NA.Count>0,])),
               " variable(s) with NA.Count>0 and Fill.Rate<1.0."))
  DQA.Summary[DQA.Summary$NA.Count>0,]
} else {
  print("No missing observations noted.")
}
## [1] "No missing observations noted."
##################################
# Checking for zero or near-zero variance Predictors
##################################
if (length(names(DQA.Predictors.Factor))==0) {
  print("No factor Predictors noted.")
} else if (nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])>0){
  print(paste0("Low variance observed for ",
               (nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])),
               " factor variable(s) with First.Second.Mode.Ratio>5."))
  DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,]
} else {
  print("No low variance factor Predictors due to high first-second mode ratio noted.")
}
## [1] "Low variance observed for 1 factor variable(s) with First.Second.Mode.Ratio>5."
##   Column.Name Column.Type Unique.Count First.Mode.Value Second.Mode.Value
## 1      GENDER      factor            2           Female                 x
##   First.Mode.Count Second.Mode.Count Unique.Count.Ratio First.Second.Mode.Ratio
## 1              198                 0              0.005                     Inf
if (length(names(DQA.Predictors.Numeric))==0) {
  print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])>0){
  print(paste0("Low variance observed for ",
               (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])),
               " numeric variable(s) with First.Second.Mode.Ratio>5."))
  DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,]
} else {
  print("No low variance numeric Predictors due to high first-second mode ratio noted.")
}
## [1] "Low variance observed for 3 numeric variable(s) with First.Second.Mode.Ratio>5."
##    Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 1       UNEMPR     numeric          372              0.939            8.256
## 13      SANSER     numeric          187              0.472          100.000
## 17      NCOMOR     numeric          216              0.545           22.100
##    Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 1              3.924               22                 2                  11.000
## 13            49.006               24                 2                  12.000
## 17             6.800               30                 5                   6.000
##    Minimum   Mean Median Maximum Skewness Kurtosis Percentile25th
## 1    0.071  7.749  5.648  41.153    1.758    3.711          3.575
## 13   8.632 77.606 91.239 100.000   -1.129   -0.139         63.898
## 17   4.400 19.986 19.850  58.400    0.870    1.558         13.600
##    Percentile75th
## 1           9.840
## 13         98.648
## 17         24.025
if (length(names(DQA.Predictors.Numeric))==0) {
  print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])>0){
  print(paste0("Low variance observed for ",
               (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])),
               " numeric variable(s) with Unique.Count.Ratio<0.01."))
  DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,]
} else {
  print("No low variance numeric Predictors due to low unique count ratio noted.")
}
## [1] "No low variance numeric Predictors due to low unique count ratio noted."
##################################
# Checking for skewed Predictors
##################################
if (length(names(DQA.Predictors.Numeric))==0) {
  print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
                                               as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])>0){
  print(paste0("High skewness observed for ",
  (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
                                               as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])),
  " numeric variable(s) with Skewness>3 or Skewness<(-3)."))
  DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
                                 as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),]
} else {
  print("No skewed numeric Predictors noted.")
}
## [1] "High skewness observed for 2 numeric variable(s) with Skewness>3 or Skewness<(-3)."
##   Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 3         GDP     numeric          193              0.487         1390.000
## 4         GNI     numeric          192              0.485         2040.000
##   Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 3            18.799                4                 2                   2.000
## 4           316.000                8                 4                   2.000
##   Minimum    Mean Median   Maximum Skewness Kurtosis Percentile25th
## 3   0.188 576.200 39.162 23200.000    7.506   59.379         11.315
## 4   0.375 597.197 40.068 23400.000    7.467   59.073         11.141
##   Percentile75th
## 3        250.000
## 4        246.000

1.3.3 Data Preprocessing


1.3.3.1 Outlier Treatment


Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- LED

##################################
# Gathering descriptive statistics
##################################
(DPA_Skimmed <- skim(DPA))
Data summary
Name DPA
Number of rows 396
Number of columns 22
_______________________
Column type frequency:
character 1
factor 2
numeric 19
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
COUNTRY 0 1 4 30 0 198 0

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
YEAR 0 1 FALSE 1 201: 396
GENDER 0 1 FALSE 2 Mal: 198, Fem: 198

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
LIFEXP 0 1 73.10 7.82 51.20 67.62 74.38 79.31 88.10 ▁▃▆▇▃
UNEMPR 0 1 7.75 6.34 0.07 3.58 5.65 9.84 41.15 ▇▂▁▁▁
INFMOR 0 1 21.46 18.66 1.40 5.90 15.05 30.38 88.80 ▇▃▂▁▁
GDP 0 1 576.20 2504.77 0.19 11.31 39.16 250.00 23200.00 ▇▁▁▁▁
GNI 0 1 597.20 2530.16 0.38 11.14 40.07 246.00 23400.00 ▇▁▁▁▁
CLTECH 0 1 65.83 36.32 0.00 33.50 80.10 100.00 100.00 ▃▁▁▂▇
PERCAP 0 1 16.92 24.49 0.23 2.23 6.62 19.58 175.81 ▇▁▁▁▁
RTIMOR 0 1 16.98 10.32 0.00 8.20 15.95 23.90 64.60 ▇▇▅▁▁
TUBINC 0 1 102.98 133.53 0.00 12.00 46.00 140.00 654.00 ▇▂▁▁▁
DPTIMM 0 1 87.90 12.38 35.00 85.69 92.00 97.00 99.00 ▁▁▁▃▇
HEPIMM 0 1 86.65 12.69 35.00 81.31 91.00 96.00 99.00 ▁▁▁▃▇
MEAIMM 0 1 87.22 13.14 37.00 84.85 92.00 96.00 99.00 ▁▁▁▃▇
HOSBED 0 1 2.99 2.35 0.20 1.30 2.57 3.75 13.71 ▇▅▂▁▁
SANSER 0 1 77.61 27.61 8.63 63.90 91.24 98.65 100.00 ▁▁▁▂▇
TUBTRT 0 1 77.66 16.93 0.00 73.00 82.00 88.00 100.00 ▁▁▁▅▇
URBPOP 0 1 59.21 23.24 13.25 41.61 58.90 78.10 100.00 ▅▆▇▇▆
RURPOP 0 1 40.79 23.24 0.00 21.90 41.10 58.39 86.75 ▆▇▇▆▅
NCOMOR 0 1 19.99 8.40 4.40 13.60 19.85 24.02 58.40 ▅▇▂▁▁
SUIRAT 0 1 9.34 9.01 0.00 3.30 6.95 11.22 63.00 ▇▂▁▁▁
##################################
# Outlier Treatment
##################################

##################################
# Listing all Predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("COUNTRY","YEAR","LIFEXP")]

##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]

##################################
# Identifying outliers for the numeric predictors
##################################
OutlierCountList <- c()

for (i in 1:ncol(DPA.Predictors.Numeric)) {
  Outliers <- boxplot.stats(DPA.Predictors.Numeric[,i])$out
  OutlierCount <- length(Outliers)
  OutlierCountList <- append(OutlierCountList,OutlierCount)
  OutlierIndices <- which(DPA.Predictors.Numeric[,i] %in% c(Outliers))
  print(
  ggplot(DPA.Predictors.Numeric, aes(x=DPA.Predictors.Numeric[,i])) +
  geom_boxplot() +
  theme_bw() +
  theme(axis.text.y=element_blank(), 
        axis.ticks.y=element_blank()) +
  xlab(names(DPA.Predictors.Numeric)[i]) +
  labs(title=names(DPA.Predictors.Numeric)[i],
       subtitle=paste0(OutlierCount, " Outlier(s) Detected")))
}

##################################
# Formulating the histogram
# for the numeric predictors
##################################

for (i in 1:ncol(DPA.Predictors.Numeric)) {
  Median <- format(round(median(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
  Mean <- format(round(mean(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
  Skewness <- format(round(skewness(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
  print(
  ggplot(DPA.Predictors.Numeric, aes(x=DPA.Predictors.Numeric[,i])) +
  geom_histogram(binwidth=1,color="black", fill="white") +
  geom_vline(aes(xintercept=mean(DPA.Predictors.Numeric[,i])),
            color="blue", size=1) +
    geom_vline(aes(xintercept=median(DPA.Predictors.Numeric[,i])),
            color="red", size=1) +
  theme_bw() +
  ylab("Count") +
  xlab(names(DPA.Predictors.Numeric)[i]) +
  labs(title=names(DPA.Predictors.Numeric)[i],
       subtitle=paste0("Median = ", Median,
                       ", Mean = ", Mean,
                       ", Skewness = ", Skewness)))
}

##################################
# Investigating distributional anomalies
# observed for several predictors 
##################################
(INFMOR_Unique <- DPA %>%
  group_by(INFMOR) %>%
  summarize(Distinct_INFMOR = n_distinct(COUNTRY)) %>%
  arrange(desc(Distinct_INFMOR)) %>%
  slice(1:5))
## # A tibble: 5 x 2
##   INFMOR Distinct_INFMOR
##    <dbl>           <int>
## 1   30.2              14
## 2    2.1               7
## 3    6.4               6
## 4    1.7               4
## 5    2.5               4
(INFMOR_Unique_Country <- DPA[round(DPA$INFMOR,digits=1)==30.2,c("COUNTRY")])
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Channel Islands"          "Faroe Islands"           
##  [5] "French Polynesia"         "Guam"                    
##  [7] "Hong Kong SAR, China"     "Kosovo"                  
##  [9] "Liechtenstein"            "Macao SAR, China"        
## [11] "New Caledonia"            "Puerto Rico"             
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"   
## [15] "Aruba"                    "Bermuda"                 
## [17] "Channel Islands"          "Faroe Islands"           
## [19] "French Polynesia"         "Guam"                    
## [21] "Hong Kong SAR, China"     "Kosovo"                  
## [23] "Liechtenstein"            "Macao SAR, China"        
## [25] "New Caledonia"            "Puerto Rico"             
## [27] "St. Martin (French part)" "Virgin Islands (U.S.)"
DPA %>%
  group_by(CLTECH) %>%
  summarize(Distinct_CLTECH = n_distinct(COUNTRY)) %>%
  arrange(desc(Distinct_CLTECH)) %>%
  slice(1:5)
## # A tibble: 5 x 2
##   CLTECH Distinct_CLTECH
##    <dbl>           <int>
## 1 100                 55
## 2  60.6               17
## 3   9.30               3
## 4  99.9                3
## 5   0.2                2
(CLTECH_Unique_Country <- DPA[round(DPA$CLTECH,digits=1)==60.6,c("COUNTRY")])
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Channel Islands"          "Faroe Islands"           
##  [5] "French Polynesia"         "Guam"                    
##  [7] "Hong Kong SAR, China"     "Kosovo"                  
##  [9] "Lebanon"                  "Libya"                   
## [11] "Liechtenstein"            "Macao SAR, China"        
## [13] "New Caledonia"            "Puerto Rico"             
## [15] "St. Martin (French part)" "Virgin Islands (U.S.)"   
## [17] "West Bank and Gaza"       "Aruba"                   
## [19] "Bermuda"                  "Channel Islands"         
## [21] "Faroe Islands"            "French Polynesia"        
## [23] "Guam"                     "Hong Kong SAR, China"    
## [25] "Kosovo"                   "Lebanon"                 
## [27] "Libya"                    "Liechtenstein"           
## [29] "Macao SAR, China"         "New Caledonia"           
## [31] "Puerto Rico"              "St. Martin (French part)"
## [33] "Virgin Islands (U.S.)"    "West Bank and Gaza"
DPA %>%
  group_by(RTIMOR) %>%
  summarize(Distinct_RTIMOR = n_distinct(COUNTRY)) %>%
  arrange(desc(Distinct_RTIMOR)) %>%
  slice(1:5)
## # A tibble: 5 x 2
##   RTIMOR Distinct_RTIMOR
##    <dbl>           <int>
## 1   18.2              14
## 2    3.9               3
## 3    5.1               3
## 4    5.3               3
## 5   12.7               3
(RTIMOR_Unique_Country <- DPA[round(DPA$RTIMOR,digits=1)==18.2,c("COUNTRY")])
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Channel Islands"          "Faroe Islands"           
##  [5] "French Polynesia"         "Guam"                    
##  [7] "Hong Kong SAR, China"     "Kosovo"                  
##  [9] "Liechtenstein"            "Macao SAR, China"        
## [11] "New Caledonia"            "Puerto Rico"             
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"   
## [15] "Aruba"                    "Bermuda"                 
## [17] "Channel Islands"          "Faroe Islands"           
## [19] "French Polynesia"         "Guam"                    
## [21] "Hong Kong SAR, China"     "Kosovo"                  
## [23] "Liechtenstein"            "Macao SAR, China"        
## [25] "New Caledonia"            "Puerto Rico"             
## [27] "St. Martin (French part)" "Virgin Islands (U.S.)"
DPA %>%
  group_by(DPTIMM) %>%
  summarize(Distinct_DPTIMM = n_distinct(COUNTRY)) %>%
  arrange(desc(Distinct_DPTIMM)) %>%
  slice(1:5)
## # A tibble: 5 x 2
##   DPTIMM Distinct_DPTIMM
##    <dbl>           <int>
## 1   99                22
## 2   85.7              15
## 3   97                14
## 4   98                14
## 5   95                13
(DPTIMM_Unique_Country <- DPA[round(DPA$DPTIMM,digits=1)==85.7,c("COUNTRY")])
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Channel Islands"          "Faroe Islands"           
##  [5] "French Polynesia"         "Guam"                    
##  [7] "Hong Kong SAR, China"     "Kosovo"                  
##  [9] "Liechtenstein"            "Macao SAR, China"        
## [11] "New Caledonia"            "Puerto Rico"             
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"   
## [15] "West Bank and Gaza"       "Aruba"                   
## [17] "Bermuda"                  "Channel Islands"         
## [19] "Faroe Islands"            "French Polynesia"        
## [21] "Guam"                     "Hong Kong SAR, China"    
## [23] "Kosovo"                   "Liechtenstein"           
## [25] "Macao SAR, China"         "New Caledonia"           
## [27] "Puerto Rico"              "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)"    "West Bank and Gaza"
DPA %>%
  group_by(HEPIMM) %>%
  summarize(Distinct_HEPIMM = n_distinct(COUNTRY)) %>%
  arrange(desc(Distinct_HEPIMM)) %>%
  slice(1:5)
## # A tibble: 5 x 2
##   HEPIMM Distinct_HEPIMM
##    <dbl>           <int>
## 1   81.3              20
## 2   99                19
## 3   97                17
## 4   98                11
## 5   92                10
(HEPIMM_Unique_Country <- DPA[round(DPA$HEPIMM,digits=1)==81.3,c("COUNTRY")])
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Channel Islands"          "Denmark"                 
##  [5] "Faroe Islands"            "Finland"                 
##  [7] "French Polynesia"         "Guam"                    
##  [9] "Hong Kong SAR, China"     "Hungary"                 
## [11] "Iceland"                  "Kosovo"                  
## [13] "Liechtenstein"            "Macao SAR, China"        
## [15] "New Caledonia"            "Puerto Rico"             
## [17] "Slovenia"                 "St. Martin (French part)"
## [19] "Virgin Islands (U.S.)"    "West Bank and Gaza"      
## [21] "Aruba"                    "Bermuda"                 
## [23] "Channel Islands"          "Denmark"                 
## [25] "Faroe Islands"            "Finland"                 
## [27] "French Polynesia"         "Guam"                    
## [29] "Hong Kong SAR, China"     "Hungary"                 
## [31] "Iceland"                  "Kosovo"                  
## [33] "Liechtenstein"            "Macao SAR, China"        
## [35] "New Caledonia"            "Puerto Rico"             
## [37] "Slovenia"                 "St. Martin (French part)"
## [39] "Virgin Islands (U.S.)"    "West Bank and Gaza"
DPA %>%
  group_by(MEAIMM) %>%
  summarize(Distinct_MEAIMM = n_distinct(COUNTRY)) %>%
  arrange(desc(Distinct_MEAIMM)) %>%
  slice(1:5)
## # A tibble: 5 x 2
##   MEAIMM Distinct_MEAIMM
##    <dbl>           <int>
## 1   99                24
## 2   84.9              15
## 3   95                14
## 4   96                14
## 5   98                13
(MEAIMM_Unique_Country <- DPA[round(DPA$MEAIMM,digits=1)==84.9,c("COUNTRY")])
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Channel Islands"          "Faroe Islands"           
##  [5] "French Polynesia"         "Guam"                    
##  [7] "Hong Kong SAR, China"     "Kosovo"                  
##  [9] "Liechtenstein"            "Macao SAR, China"        
## [11] "New Caledonia"            "Puerto Rico"             
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"   
## [15] "West Bank and Gaza"       "Aruba"                   
## [17] "Bermuda"                  "Channel Islands"         
## [19] "Faroe Islands"            "French Polynesia"        
## [21] "Guam"                     "Hong Kong SAR, China"    
## [23] "Kosovo"                   "Liechtenstein"           
## [25] "Macao SAR, China"         "New Caledonia"           
## [27] "Puerto Rico"              "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)"    "West Bank and Gaza"
DPA %>%
  group_by(HOSBED) %>%
  summarize(Distinct_HOSBED = n_distinct(COUNTRY)) %>%
  arrange(desc(Distinct_HOSBED)) %>%
  slice(1:5)
## # A tibble: 5 x 2
##   HOSBED Distinct_HOSBED
##    <dbl>           <int>
## 1   2.99              17
## 2   0.4                4
## 3   0.8                2
## 4   0.85               2
## 5   0.9                2
(HOSBED_Unique_Country <- DPA[round(DPA$HOSBED,digits=1)==3.0,c("COUNTRY")])
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Channel Islands"          "Faroe Islands"           
##  [5] "French Polynesia"         "Guam"                    
##  [7] "Hong Kong SAR, China"     "Kosovo"                  
##  [9] "Liechtenstein"            "Macao SAR, China"        
## [11] "Namibia"                  "New Caledonia"           
## [13] "Papua New Guinea"         "Puerto Rico"             
## [15] "South Sudan"              "St. Martin (French part)"
## [17] "Virgin Islands (U.S.)"    "West Bank and Gaza"      
## [19] "Aruba"                    "Bermuda"                 
## [21] "Channel Islands"          "Faroe Islands"           
## [23] "French Polynesia"         "Guam"                    
## [25] "Hong Kong SAR, China"     "Kosovo"                  
## [27] "Liechtenstein"            "Macao SAR, China"        
## [29] "Namibia"                  "New Caledonia"           
## [31] "Papua New Guinea"         "Puerto Rico"             
## [33] "South Sudan"              "St. Martin (French part)"
## [35] "Virgin Islands (U.S.)"    "West Bank and Gaza"
DPA %>%
  group_by(NCOMOR) %>%
  summarize(Distinct_NCOMOR = n_distinct(COUNTRY)) %>%
  arrange(desc(Distinct_NCOMOR)) %>%
  slice(1:5)
## # A tibble: 5 x 2
##   NCOMOR Distinct_NCOMOR
##    <dbl>           <int>
## 1   22.1              15
## 2    6.8               5
## 3   13.6               5
## 4   15.2               5
## 5   17.5               5
(NCOMOR_Unique_Country <- DPA[round(DPA$NCOMOR,digits=1)==22.1,c("COUNTRY")])
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Burkina Faso"             "Channel Islands"         
##  [5] "Faroe Islands"            "French Polynesia"        
##  [7] "Guam"                     "Hong Kong SAR, China"    
##  [9] "Kosovo"                   "Liechtenstein"           
## [11] "Macao SAR, China"         "New Caledonia"           
## [13] "Puerto Rico"              "St. Martin (French part)"
## [15] "Virgin Islands (U.S.)"    "West Bank and Gaza"      
## [17] "Aruba"                    "Bermuda"                 
## [19] "Channel Islands"          "Dominican Republic"      
## [21] "Equatorial Guinea"        "Estonia"                 
## [23] "Faroe Islands"            "French Polynesia"        
## [25] "Guam"                     "Hong Kong SAR, China"    
## [27] "Kosovo"                   "Liechtenstein"           
## [29] "Macao SAR, China"         "New Caledonia"           
## [31] "Puerto Rico"              "Sierra Leone"            
## [33] "St. Martin (French part)" "Virgin Islands (U.S.)"   
## [35] "West Bank and Gaza"
DPA %>%
  group_by(SUIRAT) %>%
  summarize(Distinct_SUIRAT = n_distinct(COUNTRY)) %>%
  arrange(desc(Distinct_SUIRAT)) %>%
  slice(1:5)
## # A tibble: 5 x 2
##   SUIRAT Distinct_SUIRAT
##    <dbl>           <int>
## 1   10.6              15
## 2    7.6               8
## 3    1.7               7
## 4    2                 7
## 5    2.8               7
(SUIRAT_Unique_Country <- DPA[round(DPA$SUIRAT,digits=1)==10.6,c("COUNTRY")])
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Channel Islands"          "Faroe Islands"           
##  [5] "French Polynesia"         "Guam"                    
##  [7] "Hong Kong SAR, China"     "Kosovo"                  
##  [9] "Liechtenstein"            "Macao SAR, China"        
## [11] "New Caledonia"            "Puerto Rico"             
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"   
## [15] "West Bank and Gaza"       "Aruba"                   
## [17] "Bermuda"                  "Channel Islands"         
## [19] "Congo, Dem. Rep."         "Faroe Islands"           
## [21] "French Polynesia"         "Guam"                    
## [23] "Hong Kong SAR, China"     "Kosovo"                  
## [25] "Liechtenstein"            "Macao SAR, China"        
## [27] "New Caledonia"            "Puerto Rico"             
## [29] "St. Martin (French part)" "Virgin Islands (U.S.)"   
## [31] "West Bank and Gaza"
(AnomalousVariables_Unique_Country <- MEAIMM_Unique_Country)
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Channel Islands"          "Faroe Islands"           
##  [5] "French Polynesia"         "Guam"                    
##  [7] "Hong Kong SAR, China"     "Kosovo"                  
##  [9] "Liechtenstein"            "Macao SAR, China"        
## [11] "New Caledonia"            "Puerto Rico"             
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"   
## [15] "West Bank and Gaza"       "Aruba"                   
## [17] "Bermuda"                  "Channel Islands"         
## [19] "Faroe Islands"            "French Polynesia"        
## [21] "Guam"                     "Hong Kong SAR, China"    
## [23] "Kosovo"                   "Liechtenstein"           
## [25] "Macao SAR, China"         "New Caledonia"           
## [27] "Puerto Rico"              "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)"    "West Bank and Gaza"
##################################
# Removing associated rows associated
# with anomalous variables
##################################
dim(DPA)
## [1] 396  22
DPA <- DPA[!(DPA$COUNTRY %in% AnomalousVariables_Unique_Country),]
dim(DPA)
## [1] 366  22
##################################
# Listing all Predictors
# for the updated data
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("COUNTRY","YEAR","LIFEXP")]

##################################
# Listing all numeric predictors
# for the updated data
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]

1.3.3.2 Zero and Near-Zero Variance


Code Chunk | Output
##################################
# Zero and Near-Zero Variance
##################################

##################################
# Identifying columns with low variance
###################################
DPA_LowVariance <- nearZeroVar(DPA,
                               freqCut = 80/20,
                               uniqueCut = 10,
                               saveMetrics= TRUE)
(DPA_LowVariance[DPA_LowVariance$nzv,])
##      freqRatio percentUnique zeroVar  nzv
## YEAR         0      0.273224    TRUE TRUE
if ((nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))==0){
  
  print("No low variance predictors noted.")
  
} else {

  print(paste0("Low variance observed for ",
               (nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
               " numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."))
  
  DPA_LowVarianceForRemoval <- (nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))
  
  print(paste0("Low variance can be resolved by removing ",
               (nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
               " numeric variable(s)."))
  
  for (j in 1:DPA_LowVarianceForRemoval) {
  DPA_LowVarianceRemovedVariable <- rownames(DPA_LowVariance[DPA_LowVariance$nzv,])[j]
  print(paste0("Variable ",
               j,
               " for removal: ",
               DPA_LowVarianceRemovedVariable))
  }
  
  DPA %>%
  skim() %>%
  dplyr::filter(skim_variable %in% rownames(DPA_LowVariance[DPA_LowVariance$nzv,]))

}
## [1] "Low variance observed for 1 numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."
## [1] "Low variance can be resolved by removing 1 numeric variable(s)."
## [1] "Variable 1 for removal: YEAR"
Data summary
Name Piped data
Number of rows 366
Number of columns 22
_______________________
Column type frequency:
factor 1
________________________
Group variables None

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
YEAR 0 1 FALSE 1 201: 366

1.3.3.3 Collinearity


Code Chunk | Output
##################################
# Collinearity
##################################

##################################
# Visualizing pairwise correlation between predictors
##################################
DPA_CorrelationTest <- cor.mtest(DPA.Predictors.Numeric,
                       method = "pearson",
                       conf.level = .95)

corrplot(cor(DPA.Predictors.Numeric,
             method = "pearson",
             use="pairwise.complete.obs"), 
         method = "circle",
         type = "upper", 
         order = "original", 
         tl.col = "black", 
         tl.cex = 0.75,
         tl.srt = 90, 
         sig.level = 0.05, 
         p.mat = DPA_CorrelationTest$p,
         insig = "blank")

##################################
# Identifying the highly correlated variables
##################################
DPA_Correlation <-  cor(DPA.Predictors.Numeric, 
                        method = "pearson",
                        use="pairwise.complete.obs")
(DPA_HighlyCorrelatedCount <- sum(abs(DPA_Correlation[upper.tri(DPA_Correlation)]) > 0.75))
## [1] 8
if (DPA_HighlyCorrelatedCount == 0) {
  print("No highly correlated predictors noted.")
} else {
  print(paste0("High correlation observed for ",
               (DPA_HighlyCorrelatedCount),
               " pairs of numeric variable(s) with Correlation.Coefficient>0.75."))
  
  (DPA_HighlyCorrelatedPairs <- corr_cross(DPA.Predictors.Numeric,
  max_pvalue = 0.05, 
  top = DPA_HighlyCorrelatedCount,
  rm.na = TRUE,
  grid = FALSE
))
  
}
## [1] "High correlation observed for 8 pairs of numeric variable(s) with Correlation.Coefficient>0.75."

if (DPA_HighlyCorrelatedCount > 0) {
  DPA_HighlyCorrelated <- findCorrelation(DPA_Correlation, cutoff = 0.75)
  
  (DPA_HighlyCorrelatedForRemoval <- length(DPA_HighlyCorrelated))
  
  print(paste0("High correlation can be resolved by removing ",
               (DPA_HighlyCorrelatedForRemoval),
               " numeric variable(s)."))
  
  for (j in 1:DPA_HighlyCorrelatedForRemoval) {
  DPA_HighlyCorrelatedRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_HighlyCorrelated[j]]
  print(paste0("Variable ",
               j,
               " for removal: ",
               DPA_HighlyCorrelatedRemovedVariable))
  }
  
}
## [1] "High correlation can be resolved by removing 6 numeric variable(s)."
## [1] "Variable 1 for removal: INFMOR"
## [1] "Variable 2 for removal: CLTECH"
## [1] "Variable 3 for removal: URBPOP"
## [1] "Variable 4 for removal: DPTIMM"
## [1] "Variable 5 for removal: MEAIMM"
## [1] "Variable 6 for removal: GNI"

1.3.3.4 Linear Dependencies


Code Chunk | Output
##################################
# Linear Dependencies
##################################

##################################
# Finding linear dependencies
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)

##################################
# Identifying the linearly dependent variables
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)

(DPA_LinearlyDependentCount <- length(DPA_LinearlyDependent$linearCombos))
## [1] 0
if (DPA_LinearlyDependentCount == 0) {
  print("No linearly dependent predictors noted.")
} else {
  print(paste0("Linear dependency observed for ",
               (DPA_LinearlyDependentCount),
               " subset(s) of numeric variable(s)."))
  
  for (i in 1:DPA_LinearlyDependentCount) {
    DPA_LinearlyDependentSubset <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$linearCombos[[i]]]
    print(paste0("Linear dependent variable(s) for subset ",
                 i,
                 " include: ",
                 DPA_LinearlyDependentSubset))
  }
  
}
## [1] "No linearly dependent predictors noted."
##################################
# Identifying the linearly dependent variables for removal
##################################

if (DPA_LinearlyDependentCount > 0) {
  DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
  
  DPA_LinearlyDependentForRemoval <- length(DPA_LinearlyDependent$remove)
  
  print(paste0("Linear dependency can be resolved by removing ",
               (DPA_LinearlyDependentForRemoval),
               " numeric variable(s)."))
  
  for (j in 1:DPA_LinearlyDependentForRemoval) {
  DPA_LinearlyDependentRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$remove[j]]
  print(paste0("Variable ",
               j,
               " for removal: ",
               DPA_LinearlyDependentRemovedVariable))
  }

}

1.3.3.5 Shape Transformation


Code Chunk | Output
##################################
# Shape Transformation
##################################

##################################
# Applying a Box-Cox transformation
##################################
DPA_BoxCox <- preProcess(DPA.Predictors.Numeric, method = c("BoxCox"))
DPA_BoxCoxTransformed <- predict(DPA_BoxCox, DPA.Predictors.Numeric)

for (i in 1:ncol(DPA_BoxCoxTransformed)) {
  Median <- format(round(median(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
  Mean <- format(round(mean(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
  Skewness <- format(round(skewness(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
  print(
  ggplot(DPA_BoxCoxTransformed, aes(x=DPA_BoxCoxTransformed[,i])) +
  geom_histogram(binwidth=1,color="black", fill="white") +
  geom_vline(aes(xintercept=mean(DPA_BoxCoxTransformed[,i])),
            color="blue", size=1) +
    geom_vline(aes(xintercept=median(DPA_BoxCoxTransformed[,i])),
            color="red", size=1) +
  theme_bw() +
  ylab("Count") +
  xlab(names(DPA_BoxCoxTransformed)[i]) +
  labs(title=names(DPA_BoxCoxTransformed)[i],
       subtitle=paste0("Median = ", Median,
                       ", Mean = ", Mean,
                       ", Skewness = ", Skewness)))
}

DPA_BoxCoxTransformed <- cbind(DPA_BoxCoxTransformed,DPA[,c("COUNTRY",
                                                            "YEAR",
                                                            "GENDER",
                                                            "LIFEXP")])

1.3.3.6 Pre-Processed Dataset


Code Chunk | Output
##################################
# Creating the pre-modelling
# train set
##################################
PMA <- DPA_BoxCoxTransformed[,!names(DPA_BoxCoxTransformed) %in% c("YEAR",
                                                                   "GNI",
                                                                   "DPTIMM",
                                                                   "MEAIMM",
                                                                   "RURPOP",
                                                                   "SANSER",
                                                                   "RTIMOR")]

##################################
# Gathering descriptive statistics
##################################
(PMA_Skimmed <- skim(PMA))
Data summary
Name PMA
Number of rows 366
Number of columns 15
_______________________
Column type frequency:
character 1
factor 1
numeric 13
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
COUNTRY 0 1 4 30 0 183 0

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
GENDER 0 1 FALSE 2 Mal: 183, Fem: 183

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
UNEMPR 0 1 2.12 1.21 -2.05 1.42 2.00 2.90 5.26 ▁▂▇▅▂
INFMOR 0 1 2.55 1.06 0.34 1.70 2.62 3.48 4.49 ▅▆▇▇▆
GDP 0 1 3.89 2.26 -1.67 2.55 3.80 5.55 10.05 ▂▇▇▆▁
CLTECH 0 1 66.26 37.75 0.00 30.47 84.60 100.00 100.00 ▃▁▁▂▇
PERCAP 0 1 1.77 1.41 -1.48 0.65 1.80 2.85 4.73 ▂▆▇▆▃
TUBINC 0 1 105.76 137.53 0.00 12.00 46.00 149.00 654.00 ▇▂▁▁▁
HEPIMM 0 1 3877.73 1003.98 612.00 3339.73 4231.50 4704.00 4900.00 ▁▁▁▃▇
HOSBED 0 1 0.77 0.85 -1.61 0.15 0.84 1.40 2.62 ▁▅▇▇▃
TUBTRT 0 1 77.90 17.27 0.00 73.25 83.00 88.00 100.00 ▁▁▁▅▇
URBPOP 0 1 58.40 22.69 13.25 40.37 58.52 77.78 100.00 ▅▆▇▇▅
NCOMOR 0 1 4.67 1.08 1.87 3.93 4.70 5.38 7.96 ▂▅▇▃▁
SUIRAT 0 1 9.23 9.37 0.00 3.20 6.30 11.80 63.00 ▇▂▁▁▁
LIFEXP 0 1 72.54 7.77 51.20 66.99 73.69 78.58 87.45 ▁▃▆▇▅

1.3.4 Data Exploration


Code Chunk | Output
##################################
# Loading dataset
##################################
PME <- PMA
PME.Numeric <- PME[,sapply(PME, is.numeric), drop = FALSE]

##################################
# Listing all Predictors
##################################
PME.Predictors <- PME[,!names(PME) %in% c("COUNTRY","LIFEXP")]

##################################
# Listing all numeric Predictors
##################################
PME.Predictors.Numeric <- PME.Predictors[,sapply(PME.Predictors, is.numeric), drop = FALSE]
ncol(PME.Predictors.Numeric)
## [1] 12
##################################
# Listing all numeric Predictors
##################################
PME.Predictors.Factor <- PME.Predictors[,sapply(PME.Predictors, is.factor), drop = FALSE]
ncol(PME.Predictors.Factor)
## [1] 1
##################################
# Formulating the scatter plot
##################################
featurePlot(x = PME.Predictors.Numeric, 
            y = PME$LIFEXP,
            plot = "scatter",
            type = c("p", "smooth"),
            span = .5,
            layout = c(4, 3))

##################################
# Formulating the box plot
##################################
featurePlot(x = PME.Numeric, 
            y = PME$GENDER,
            plot = "box",
            scales = list(x = list(relation="free", rot = 90), 
                          y = list(relation="free")),
            adjust = 1.5,
            layout = c(4, 4))

1.3.5 Feature Selection


1.3.5.1 Locally Weighted Scatterplot Smoothing Pseudo-R-Squared (LOWESSPR)


Code Chunk | Output
##################################
# Evaluating model-independent
# feature importance metrics
##################################

##################################
# Obtaining the LOWESSPR pseudo-R-Squared
##################################
FE_LOWESSPR <- filterVarImp(x = PME.Numeric[,!names(PME.Numeric) %in% c("LIFEXP")],
                            y = PME$LIFEXP,
                            nonpara = TRUE)

##################################
# Formulating the summary table
##################################
FE_LOWESSPR_Summary <- FE_LOWESSPR 

FE_LOWESSPR_Summary$Predictor <- rownames(FE_LOWESSPR)
names(FE_LOWESSPR_Summary)[1] <- "LOWESSPR"
FE_LOWESSPR_Summary$Metric <- rep("LOWESSPR",nrow(FE_LOWESSPR))

FE_LOWESSPR_Summary
##          LOWESSPR Predictor   Metric
## UNEMPR 0.03820513    UNEMPR LOWESSPR
## INFMOR 0.82577754    INFMOR LOWESSPR
## GDP    0.25709988       GDP LOWESSPR
## CLTECH 0.58356246    CLTECH LOWESSPR
## PERCAP 0.62203760    PERCAP LOWESSPR
## TUBINC 0.51694851    TUBINC LOWESSPR
## HEPIMM 0.18580579    HEPIMM LOWESSPR
## HOSBED 0.34778666    HOSBED LOWESSPR
## TUBTRT 0.10269120    TUBTRT LOWESSPR
## URBPOP 0.35270735    URBPOP LOWESSPR
## NCOMOR 0.59824235    NCOMOR LOWESSPR
## SUIRAT 0.06391291    SUIRAT LOWESSPR
##################################
# Exploring predictor performance
# using LOWESS
##################################
dotplot(Predictor ~ LOWESSPR | Metric, 
        FE_LOWESSPR_Summary,
        origin = 0,
        type = c("p", "h"),
        pch = 16,
        cex = 2,
        alpha = 0.45,
        prepanel = function(x, y) {
            list(ylim = levels(reorder(y, x)))
        },
        panel = function(x, y, ...) {
            panel.dotplot(x, reorder(y, x), ...)
        })


1.3.5.2 Pearson’s Correlation Coefficient (PCC)


Code Chunk | Output
##################################
# Obtaining the Pearson correlation coefficient
##################################
(FE_PCC <- abs(cor(PME.Numeric, method="pearson")[-13,13]))
##    UNEMPR    INFMOR       GDP    CLTECH    PERCAP    TUBINC    HEPIMM    HOSBED 
## 0.0173876 0.8799932 0.4607543 0.7531760 0.7852114 0.5904533 0.4310520 0.5632423 
##    TUBTRT    URBPOP    NCOMOR    SUIRAT 
## 0.3204547 0.5751200 0.7343442 0.1214945
##################################
# Formulating the summary table
##################################
FE_PCC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
                             PCC = FE_PCC,
                             Metric = rep("PCC", length(FE_PCC)))

FE_PCC_Summary
##        Predictor       PCC Metric
## UNEMPR    UNEMPR 0.0173876    PCC
## INFMOR    INFMOR 0.8799932    PCC
## GDP          GDP 0.4607543    PCC
## CLTECH    CLTECH 0.7531760    PCC
## PERCAP    PERCAP 0.7852114    PCC
## TUBINC    TUBINC 0.5904533    PCC
## HEPIMM    HEPIMM 0.4310520    PCC
## HOSBED    HOSBED 0.5632423    PCC
## TUBTRT    TUBTRT 0.3204547    PCC
## URBPOP    URBPOP 0.5751200    PCC
## NCOMOR    NCOMOR 0.7343442    PCC
## SUIRAT    SUIRAT 0.1214945    PCC
##################################
# Exploring predictor performance
# using PCC
##################################
dotplot(Predictor ~ PCC | Metric, 
        FE_PCC_Summary,
        origin = 0,
        type = c("p", "h"),
        pch = 16,
        cex = 2,
        alpha = 0.45,
        prepanel = function(x, y) {
            list(ylim = levels(reorder(y, x)))
        },
        panel = function(x, y, ...) {
            panel.dotplot(x, reorder(y, x), ...)
        })


1.3.5.3 Spearman’s Rank Correlation Coefficient (SRCC)


Code Chunk | Output
##################################
# Obtaining the Spearman's rank correlation coefficient
##################################
(FE_SRCC <- abs(cor(PME.Numeric, method="spearman")[-13,13]))
##      UNEMPR      INFMOR         GDP      CLTECH      PERCAP      TUBINC 
## 0.007171824 0.891693321 0.502535269 0.784386965 0.798496072 0.716895672 
##      HEPIMM      HOSBED      TUBTRT      URBPOP      NCOMOR      SUIRAT 
## 0.378587047 0.555525640 0.335530704 0.602733552 0.791705740 0.116688271
##################################
# Formulating the summary table
##################################
FE_SRCC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
                             SRCC = FE_SRCC,
                             Metric = rep("SRCC", length(FE_SRCC)))

FE_SRCC_Summary
##        Predictor        SRCC Metric
## UNEMPR    UNEMPR 0.007171824   SRCC
## INFMOR    INFMOR 0.891693321   SRCC
## GDP          GDP 0.502535269   SRCC
## CLTECH    CLTECH 0.784386965   SRCC
## PERCAP    PERCAP 0.798496072   SRCC
## TUBINC    TUBINC 0.716895672   SRCC
## HEPIMM    HEPIMM 0.378587047   SRCC
## HOSBED    HOSBED 0.555525640   SRCC
## TUBTRT    TUBTRT 0.335530704   SRCC
## URBPOP    URBPOP 0.602733552   SRCC
## NCOMOR    NCOMOR 0.791705740   SRCC
## SUIRAT    SUIRAT 0.116688271   SRCC
##################################
# Exploring predictor performance
# using SRCC
##################################
dotplot(Predictor ~ SRCC | Metric, 
        FE_SRCC_Summary,
        origin = 0,
        type = c("p", "h"),
        pch = 16,
        cex = 2,
        alpha = 0.45,
        prepanel = function(x, y) {
            list(ylim = levels(reorder(y, x)))
        },
        panel = function(x, y, ...) {
            panel.dotplot(x, reorder(y, x), ...)
        })


1.3.5.4 Maximal Information Coefficient (MIC)


Code Chunk | Output
##################################
# Obtaining the maximal information coefficient
##################################
FE_MIC <- mine(x = PME.Numeric[,!names(PME.Numeric) %in% c("LIFEXP")],
               y = PME$LIFEXP)$MIC

##################################
# Formulating the summary table
##################################
FE_MIC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
                             MIC = FE_MIC[,1],
                             Metric = rep("MIC", length(FE_MIC)))

FE_MIC_Summary
##    Predictor       MIC Metric
## 1     UNEMPR 0.1884410    MIC
## 2     INFMOR 0.6990431    MIC
## 3        GDP 0.3199394    MIC
## 4     CLTECH 0.5129197    MIC
## 5     PERCAP 0.5532144    MIC
## 6     TUBINC 0.5141625    MIC
## 7     HEPIMM 0.2656190    MIC
## 8     HOSBED 0.3706681    MIC
## 9     TUBTRT 0.2392547    MIC
## 10    URBPOP 0.4107627    MIC
## 11    NCOMOR 0.6458233    MIC
## 12    SUIRAT 0.2314843    MIC
##################################
# Exploring predictor performance
# using MIC
##################################
dotplot(Predictor ~ MIC | Metric, 
        FE_MIC_Summary,
        origin = 0,
        type = c("p", "h"),
        pch = 16,
        cex = 2,
        alpha = 0.45,
        prepanel = function(x, y) {
            list(ylim = levels(reorder(y, x)))
        },
        panel = function(x, y, ...) {
            panel.dotplot(x, reorder(y, x), ...)
        })


1.3.5.5 Relief Values (RV)


Code Chunk | Output
##################################
# Obtaining the relief values
##################################
FE_RV <- attrEval(LIFEXP ~ .,  
                  data = PME.Numeric,
                  estimator = "RReliefFequalK")

##################################
# Formulating the summary table
##################################
FE_RV_Summary <- data.frame(Predictor = names(FE_RV),
                            RV = FE_RV,
                            Metric = rep("RV", length(FE_RV)))

FE_RV_Summary
##        Predictor           RV Metric
## UNEMPR    UNEMPR  0.002897323     RV
## INFMOR    INFMOR  0.092217298     RV
## GDP          GDP -0.116882500     RV
## CLTECH    CLTECH  0.036861936     RV
## PERCAP    PERCAP -0.037386041     RV
## TUBINC    TUBINC  0.066678316     RV
## HEPIMM    HEPIMM -0.031239659     RV
## HOSBED    HOSBED -0.052866119     RV
## TUBTRT    TUBTRT -0.149965561     RV
## URBPOP    URBPOP -0.105354115     RV
## NCOMOR    NCOMOR  0.279415774     RV
## SUIRAT    SUIRAT  0.110919970     RV
##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ RV | Metric, 
        FE_RV_Summary,
        origin = 0,
        type = c("p", "h"),
        pch = 16,
        cex = 2,
        alpha = 0.45,
        prepanel = function(x, y) {
            list(ylim = levels(reorder(y, x)))
        },
        panel = function(x, y, ...) {
            panel.dotplot(x, reorder(y, x), ...)
        })

1.3.6 Model Development and Performance Estimation


1.3.6.1 Stochastic Gradient Boosting (GBM)


Code Chunk | Output
##################################
# Preparing the dataset for
# model development and test
##################################
set.seed(12345678)
trainIndex <- createDataPartition(PME$LIFEXP,
                                  p = 0.8, 
                                  list = FALSE, 
                                  times = 1)

##################################
# Formulating the model development data
##################################
MD <- PME[ trainIndex,]

##################################
# Formulating the model test data
##################################
MT <- PME[-trainIndex,]

##################################
# Preparing the dataset for
# model development
##################################
MD <- MD[,c("GENDER","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")]

MD.Model.Predictors <- MD[,c("GENDER","INFMOR","PERCAP","CLTECH","NCOMOR")]

##################################
# Preparing the dataset for
# model test
##################################
MT <- MT[,c("GENDER","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")]

MT.Model.Predictors <- MT[,c("GENDER","INFMOR","PERCAP","CLTECH","NCOMOR")]

##################################
# Creating consistent fold assignments 
# for the 10-Fold Cross Validation process
##################################
set.seed(12345678)
KFold_Indices <- createFolds(MD$LIFEXP,
                             k = 10,
                             returnTrain=TRUE)
KFold_Control <- trainControl(method="cv",
                              index=KFold_Indices)

##################################
# Defining the model hyperparameter values
# for the GBM model
##################################
GBM_Grid = expand.grid(n.trees = c(100, 200, 300), 
                       interaction.depth = c(1, 3, 5),
                       shrinkage = c(0.10,0.05,0.01),
                       n.minobsinnode = c(15,10,5))

##################################
# Running the GBM model
# by setting the caret method to 'gbm'
##################################
set.seed(12345678)
GBM_Tune <- train(x = MD.Model.Predictors, 
                  y = MD$LIFEXP,
                  method = "gbm",
                  tuneGrid = GBM_Grid,
                  trControl = KFold_Control)
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.1227             nan     0.0100    0.7693
##      2       61.3337             nan     0.0100    0.6983
##      3       60.6061             nan     0.0100    0.6633
##      4       59.7836             nan     0.0100    0.7341
##      5       59.0098             nan     0.0100    0.7139
##      6       58.2788             nan     0.0100    0.6988
##      7       57.5535             nan     0.0100    0.7128
##      8       56.9286             nan     0.0100    0.7258
##      9       56.1944             nan     0.0100    0.6810
##     10       55.5511             nan     0.0100    0.7079
##     20       49.5540             nan     0.0100    0.5830
##     40       39.9875             nan     0.0100    0.3742
##     60       32.7024             nan     0.0100    0.2837
##     80       27.1565             nan     0.0100    0.2605
##    100       22.8699             nan     0.0100    0.1831
##    120       19.6043             nan     0.0100    0.1340
##    140       16.9933             nan     0.0100    0.1047
##    160       14.8505             nan     0.0100    0.0993
##    180       13.2168             nan     0.0100    0.0699
##    200       11.8566             nan     0.0100    0.0406
##    220       10.7300             nan     0.0100    0.0462
##    240        9.7666             nan     0.0100    0.0397
##    260        8.9376             nan     0.0100    0.0338
##    280        8.2565             nan     0.0100    0.0243
##    300        7.6740             nan     0.0100    0.0186
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.0988             nan     0.0100    0.7940
##      2       61.3156             nan     0.0100    0.7742
##      3       60.5943             nan     0.0100    0.7824
##      4       59.7818             nan     0.0100    0.7272
##      5       59.0743             nan     0.0100    0.7264
##      6       58.3737             nan     0.0100    0.6166
##      7       57.6752             nan     0.0100    0.6979
##      8       56.9330             nan     0.0100    0.7227
##      9       56.2539             nan     0.0100    0.7127
##     10       55.5820             nan     0.0100    0.6156
##     20       49.4017             nan     0.0100    0.5509
##     40       39.8044             nan     0.0100    0.3929
##     60       32.6954             nan     0.0100    0.2998
##     80       27.0214             nan     0.0100    0.2255
##    100       22.8076             nan     0.0100    0.1919
##    120       19.5829             nan     0.0100    0.1334
##    140       16.8504             nan     0.0100    0.0961
##    160       14.7563             nan     0.0100    0.0761
##    180       13.0689             nan     0.0100    0.0810
##    200       11.6840             nan     0.0100    0.0492
##    220       10.5358             nan     0.0100    0.0446
##    240        9.5983             nan     0.0100    0.0374
##    260        8.8306             nan     0.0100    0.0197
##    280        8.1503             nan     0.0100    0.0261
##    300        7.6018             nan     0.0100    0.0186
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.1245             nan     0.0100    0.8097
##      2       61.2860             nan     0.0100    0.8222
##      3       60.5177             nan     0.0100    0.7625
##      4       59.8337             nan     0.0100    0.7230
##      5       59.1519             nan     0.0100    0.7023
##      6       58.3911             nan     0.0100    0.6609
##      7       57.7117             nan     0.0100    0.7349
##      8       56.9769             nan     0.0100    0.7364
##      9       56.3176             nan     0.0100    0.6742
##     10       55.5860             nan     0.0100    0.6828
##     20       49.3356             nan     0.0100    0.5617
##     40       39.8248             nan     0.0100    0.4355
##     60       32.5515             nan     0.0100    0.3474
##     80       27.0736             nan     0.0100    0.2165
##    100       22.8876             nan     0.0100    0.1807
##    120       19.5812             nan     0.0100    0.1292
##    140       17.0003             nan     0.0100    0.0993
##    160       14.9433             nan     0.0100    0.0780
##    180       13.2105             nan     0.0100    0.0633
##    200       11.7907             nan     0.0100    0.0527
##    220       10.6580             nan     0.0100    0.0298
##    240        9.7092             nan     0.0100    0.0303
##    260        8.9433             nan     0.0100    0.0293
##    280        8.2702             nan     0.0100    0.0224
##    300        7.6993             nan     0.0100    0.0110
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.8484             nan     0.0100    1.0058
##      2       60.8409             nan     0.0100    0.9750
##      3       59.8266             nan     0.0100    1.0107
##      4       58.8552             nan     0.0100    0.9944
##      5       57.8969             nan     0.0100    0.8557
##      6       56.9014             nan     0.0100    1.0323
##      7       56.0024             nan     0.0100    0.8546
##      8       55.1274             nan     0.0100    0.9597
##      9       54.1694             nan     0.0100    0.9515
##     10       53.3205             nan     0.0100    0.8718
##     20       45.4893             nan     0.0100    0.7193
##     40       33.3136             nan     0.0100    0.4897
##     60       25.1491             nan     0.0100    0.2779
##     80       19.1531             nan     0.0100    0.2291
##    100       15.0084             nan     0.0100    0.1369
##    120       12.0067             nan     0.0100    0.1216
##    140        9.8430             nan     0.0100    0.0754
##    160        8.2437             nan     0.0100    0.0535
##    180        7.1110             nan     0.0100    0.0512
##    200        6.2282             nan     0.0100    0.0366
##    220        5.6014             nan     0.0100    0.0207
##    240        5.1055             nan     0.0100    0.0170
##    260        4.7446             nan     0.0100    0.0102
##    280        4.4574             nan     0.0100    0.0061
##    300        4.2377             nan     0.0100   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.8964             nan     0.0100    0.9073
##      2       60.9011             nan     0.0100    0.9375
##      3       59.9218             nan     0.0100    0.8415
##      4       58.9196             nan     0.0100    1.0333
##      5       57.9778             nan     0.0100    0.9587
##      6       57.0334             nan     0.0100    0.9181
##      7       56.1133             nan     0.0100    0.9913
##      8       55.2123             nan     0.0100    0.8484
##      9       54.3694             nan     0.0100    0.7972
##     10       53.4861             nan     0.0100    0.8989
##     20       45.4135             nan     0.0100    0.7464
##     40       33.3772             nan     0.0100    0.4940
##     60       25.0348             nan     0.0100    0.3264
##     80       19.1679             nan     0.0100    0.2903
##    100       15.0754             nan     0.0100    0.2075
##    120       12.1305             nan     0.0100    0.1155
##    140        9.9898             nan     0.0100    0.0771
##    160        8.4204             nan     0.0100    0.0615
##    180        7.2428             nan     0.0100    0.0401
##    200        6.3844             nan     0.0100    0.0321
##    220        5.7542             nan     0.0100    0.0188
##    240        5.2573             nan     0.0100    0.0117
##    260        4.9011             nan     0.0100    0.0102
##    280        4.6129             nan     0.0100    0.0003
##    300        4.4132             nan     0.0100    0.0027
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.8232             nan     0.0100    1.0853
##      2       60.7615             nan     0.0100    1.0726
##      3       59.7809             nan     0.0100    1.0614
##      4       58.7383             nan     0.0100    0.9429
##      5       57.7943             nan     0.0100    1.0443
##      6       56.8382             nan     0.0100    0.9865
##      7       55.9356             nan     0.0100    0.9874
##      8       55.0589             nan     0.0100    0.8711
##      9       54.1475             nan     0.0100    0.8512
##     10       53.2693             nan     0.0100    0.8231
##     20       45.5233             nan     0.0100    0.7284
##     40       33.3373             nan     0.0100    0.5334
##     60       25.0338             nan     0.0100    0.3390
##     80       19.1644             nan     0.0100    0.2640
##    100       15.0638             nan     0.0100    0.1606
##    120       12.1221             nan     0.0100    0.1083
##    140       10.0718             nan     0.0100    0.0768
##    160        8.5848             nan     0.0100    0.0642
##    180        7.4393             nan     0.0100    0.0463
##    200        6.5853             nan     0.0100    0.0221
##    220        5.9490             nan     0.0100    0.0210
##    240        5.4649             nan     0.0100    0.0117
##    260        5.0887             nan     0.0100    0.0037
##    280        4.8247             nan     0.0100   -0.0003
##    300        4.6158             nan     0.0100    0.0061
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.8567             nan     0.0100    1.0966
##      2       60.7933             nan     0.0100    1.1470
##      3       59.7505             nan     0.0100    1.1401
##      4       58.7434             nan     0.0100    0.9251
##      5       57.7391             nan     0.0100    1.0180
##      6       56.7267             nan     0.0100    1.0002
##      7       55.7543             nan     0.0100    0.9079
##      8       54.7552             nan     0.0100    0.9434
##      9       53.8335             nan     0.0100    0.9930
##     10       52.9253             nan     0.0100    0.8661
##     20       44.5694             nan     0.0100    0.6188
##     40       32.0461             nan     0.0100    0.5462
##     60       23.4480             nan     0.0100    0.3912
##     80       17.4106             nan     0.0100    0.2258
##    100       13.2948             nan     0.0100    0.1607
##    120       10.4197             nan     0.0100    0.0962
##    140        8.3610             nan     0.0100    0.0711
##    160        6.9310             nan     0.0100    0.0478
##    180        5.9367             nan     0.0100    0.0349
##    200        5.1993             nan     0.0100    0.0156
##    220        4.6451             nan     0.0100    0.0128
##    240        4.2325             nan     0.0100    0.0075
##    260        3.9176             nan     0.0100    0.0065
##    280        3.6660             nan     0.0100    0.0060
##    300        3.4646             nan     0.0100    0.0044
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.8350             nan     0.0100    1.0879
##      2       60.7413             nan     0.0100    1.0083
##      3       59.6438             nan     0.0100    1.0774
##      4       58.5917             nan     0.0100    0.9423
##      5       57.5165             nan     0.0100    0.9410
##      6       56.5438             nan     0.0100    0.9834
##      7       55.5377             nan     0.0100    0.9295
##      8       54.5458             nan     0.0100    0.8482
##      9       53.5609             nan     0.0100    0.8309
##     10       52.6312             nan     0.0100    0.8911
##     20       44.3743             nan     0.0100    0.7673
##     40       31.8672             nan     0.0100    0.4832
##     60       23.2646             nan     0.0100    0.3351
##     80       17.3926             nan     0.0100    0.2176
##    100       13.2413             nan     0.0100    0.1499
##    120       10.4238             nan     0.0100    0.1254
##    140        8.4523             nan     0.0100    0.0771
##    160        7.0427             nan     0.0100    0.0488
##    180        6.0645             nan     0.0100    0.0377
##    200        5.3543             nan     0.0100    0.0211
##    220        4.8252             nan     0.0100    0.0136
##    240        4.4383             nan     0.0100    0.0061
##    260        4.1290             nan     0.0100    0.0058
##    280        3.9063             nan     0.0100    0.0012
##    300        3.7428             nan     0.0100    0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.7795             nan     0.0100    1.1843
##      2       60.7281             nan     0.0100    1.0092
##      3       59.6082             nan     0.0100    1.0506
##      4       58.5483             nan     0.0100    1.0928
##      5       57.5230             nan     0.0100    1.0513
##      6       56.5212             nan     0.0100    0.9589
##      7       55.5274             nan     0.0100    1.0002
##      8       54.6074             nan     0.0100    0.9283
##      9       53.6685             nan     0.0100    1.0439
##     10       52.7635             nan     0.0100    0.9825
##     20       44.5357             nan     0.0100    0.7147
##     40       32.1380             nan     0.0100    0.4751
##     60       23.5894             nan     0.0100    0.3759
##     80       17.6773             nan     0.0100    0.2295
##    100       13.6115             nan     0.0100    0.1471
##    120       10.7324             nan     0.0100    0.0983
##    140        8.7455             nan     0.0100    0.0588
##    160        7.3378             nan     0.0100    0.0603
##    180        6.3314             nan     0.0100    0.0440
##    200        5.6190             nan     0.0100    0.0307
##    220        5.1231             nan     0.0100    0.0156
##    240        4.7532             nan     0.0100    0.0019
##    260        4.4648             nan     0.0100    0.0116
##    280        4.2383             nan     0.0100    0.0045
##    300        4.0646             nan     0.0100   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.0285             nan     0.0500    3.7847
##      2       55.2280             nan     0.0500    3.4680
##      3       52.0630             nan     0.0500    3.0705
##      4       49.0940             nan     0.0500    2.7595
##      5       46.3520             nan     0.0500    2.7705
##      6       43.8739             nan     0.0500    2.3552
##      7       41.5192             nan     0.0500    2.3073
##      8       39.4099             nan     0.0500    2.0434
##      9       37.5930             nan     0.0500    1.7674
##     10       35.7126             nan     0.0500    1.8581
##     20       22.3387             nan     0.0500    0.9188
##     40       11.6088             nan     0.0500    0.3617
##     60        7.4938             nan     0.0500    0.0612
##     80        5.7633             nan     0.0500    0.0581
##    100        4.9643             nan     0.0500    0.0286
##    120        4.5650             nan     0.0500    0.0075
##    140        4.3793             nan     0.0500   -0.0163
##    160        4.2708             nan     0.0500   -0.0097
##    180        4.1721             nan     0.0500   -0.0002
##    200        4.1168             nan     0.0500   -0.0102
##    220        4.0538             nan     0.0500   -0.0184
##    240        4.0048             nan     0.0500   -0.0171
##    260        3.9464             nan     0.0500   -0.0345
##    280        3.9020             nan     0.0500   -0.0108
##    300        3.8507             nan     0.0500   -0.0057
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.2877             nan     0.0500    3.7427
##      2       55.8983             nan     0.0500    3.4934
##      3       52.5998             nan     0.0500    3.1757
##      4       49.7127             nan     0.0500    2.8915
##      5       46.9318             nan     0.0500    2.6198
##      6       44.5185             nan     0.0500    2.1359
##      7       41.9770             nan     0.0500    2.3058
##      8       39.8183             nan     0.0500    2.3300
##      9       37.9860             nan     0.0500    1.7638
##     10       35.9557             nan     0.0500    1.8572
##     20       22.8119             nan     0.0500    0.8712
##     40       11.5042             nan     0.0500    0.3279
##     60        7.5010             nan     0.0500    0.0908
##     80        5.8261             nan     0.0500    0.0300
##    100        5.0749             nan     0.0500   -0.0070
##    120        4.6814             nan     0.0500   -0.0055
##    140        4.5318             nan     0.0500   -0.0097
##    160        4.3929             nan     0.0500   -0.0004
##    180        4.3060             nan     0.0500   -0.0035
##    200        4.2341             nan     0.0500   -0.0337
##    220        4.1675             nan     0.0500   -0.0103
##    240        4.1152             nan     0.0500   -0.0162
##    260        4.0526             nan     0.0500   -0.0057
##    280        4.0087             nan     0.0500   -0.0063
##    300        3.9599             nan     0.0500   -0.0075
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.9434             nan     0.0500    4.1606
##      2       55.6102             nan     0.0500    3.6048
##      3       52.3485             nan     0.0500    3.2391
##      4       49.4775             nan     0.0500    3.1181
##      5       46.8498             nan     0.0500    2.5629
##      6       44.3032             nan     0.0500    2.3166
##      7       41.9600             nan     0.0500    1.9276
##      8       39.6646             nan     0.0500    2.2852
##      9       37.5780             nan     0.0500    2.0908
##     10       35.7240             nan     0.0500    1.7903
##     20       22.5620             nan     0.0500    0.9948
##     40       11.7572             nan     0.0500    0.2593
##     60        7.6888             nan     0.0500    0.1064
##     80        6.0366             nan     0.0500    0.0430
##    100        5.2608             nan     0.0500   -0.0022
##    120        4.8910             nan     0.0500   -0.0164
##    140        4.7140             nan     0.0500   -0.0105
##    160        4.6063             nan     0.0500   -0.0039
##    180        4.5044             nan     0.0500   -0.0144
##    200        4.4191             nan     0.0500   -0.0043
##    220        4.3510             nan     0.0500   -0.0239
##    240        4.2914             nan     0.0500   -0.0222
##    260        4.2361             nan     0.0500   -0.0179
##    280        4.1922             nan     0.0500   -0.0030
##    300        4.1440             nan     0.0500   -0.0071
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.6101             nan     0.0500    4.6628
##      2       53.2366             nan     0.0500    4.5087
##      3       49.2002             nan     0.0500    4.5266
##      4       45.3188             nan     0.0500    3.6167
##      5       42.0193             nan     0.0500    3.6309
##      6       39.0588             nan     0.0500    3.1434
##      7       35.9386             nan     0.0500    2.6117
##      8       33.3803             nan     0.0500    2.4874
##      9       30.7484             nan     0.0500    2.6640
##     10       28.6123             nan     0.0500    1.9950
##     20       14.7083             nan     0.0500    0.8919
##     40        6.2055             nan     0.0500    0.1508
##     60        4.2665             nan     0.0500    0.0072
##     80        3.5928             nan     0.0500   -0.0036
##    100        3.3012             nan     0.0500   -0.0211
##    120        3.0554             nan     0.0500   -0.0101
##    140        2.8697             nan     0.0500   -0.0080
##    160        2.6751             nan     0.0500    0.0018
##    180        2.5134             nan     0.0500   -0.0095
##    200        2.3650             nan     0.0500   -0.0143
##    220        2.2550             nan     0.0500   -0.0048
##    240        2.1278             nan     0.0500   -0.0187
##    260        2.0159             nan     0.0500   -0.0129
##    280        1.9232             nan     0.0500   -0.0215
##    300        1.8330             nan     0.0500   -0.0050
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.7043             nan     0.0500    5.4844
##      2       53.0352             nan     0.0500    4.7683
##      3       48.7327             nan     0.0500    4.1390
##      4       44.8769             nan     0.0500    3.9403
##      5       41.5176             nan     0.0500    3.4863
##      6       38.3935             nan     0.0500    2.6453
##      7       35.6388             nan     0.0500    2.7774
##      8       33.0377             nan     0.0500    2.6318
##      9       30.5692             nan     0.0500    2.2881
##     10       28.3643             nan     0.0500    2.0766
##     20       14.6763             nan     0.0500    0.8302
##     40        6.1216             nan     0.0500    0.1411
##     60        4.2930             nan     0.0500    0.0187
##     80        3.7666             nan     0.0500   -0.0051
##    100        3.4912             nan     0.0500   -0.0239
##    120        3.2857             nan     0.0500   -0.0090
##    140        3.1191             nan     0.0500   -0.0111
##    160        2.9533             nan     0.0500   -0.0189
##    180        2.8280             nan     0.0500   -0.0113
##    200        2.7362             nan     0.0500   -0.0128
##    220        2.6297             nan     0.0500   -0.0116
##    240        2.5261             nan     0.0500   -0.0172
##    260        2.4411             nan     0.0500   -0.0207
##    280        2.3411             nan     0.0500   -0.0067
##    300        2.2573             nan     0.0500   -0.0244
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.5689             nan     0.0500    4.5040
##      2       52.8280             nan     0.0500    4.4066
##      3       48.7064             nan     0.0500    4.4652
##      4       44.8798             nan     0.0500    3.5547
##      5       41.3601             nan     0.0500    3.4924
##      6       38.1744             nan     0.0500    3.1494
##      7       35.3853             nan     0.0500    2.6172
##      8       32.7987             nan     0.0500    2.4956
##      9       30.4805             nan     0.0500    2.1042
##     10       28.3960             nan     0.0500    2.0829
##     20       14.7823             nan     0.0500    0.7540
##     40        6.3443             nan     0.0500    0.1509
##     60        4.5748             nan     0.0500    0.0215
##     80        4.0581             nan     0.0500   -0.0097
##    100        3.7625             nan     0.0500   -0.0099
##    120        3.5690             nan     0.0500   -0.0071
##    140        3.4191             nan     0.0500   -0.0278
##    160        3.2920             nan     0.0500   -0.0087
##    180        3.1607             nan     0.0500   -0.0149
##    200        3.0388             nan     0.0500   -0.0067
##    220        2.9503             nan     0.0500   -0.0075
##    240        2.8705             nan     0.0500   -0.0194
##    260        2.7842             nan     0.0500   -0.0290
##    280        2.7023             nan     0.0500   -0.0095
##    300        2.6160             nan     0.0500   -0.0091
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.6070             nan     0.0500    5.5350
##      2       52.9644             nan     0.0500    4.4937
##      3       48.6983             nan     0.0500    4.6190
##      4       44.3573             nan     0.0500    3.6627
##      5       40.5895             nan     0.0500    4.0002
##      6       37.0560             nan     0.0500    3.2714
##      7       34.1248             nan     0.0500    3.2003
##      8       31.3187             nan     0.0500    2.5264
##      9       28.9119             nan     0.0500    2.4922
##     10       26.6396             nan     0.0500    2.1793
##     20       12.8245             nan     0.0500    0.8007
##     40        5.0838             nan     0.0500    0.0937
##     60        3.4329             nan     0.0500    0.0155
##     80        2.9072             nan     0.0500   -0.0065
##    100        2.5530             nan     0.0500   -0.0492
##    120        2.3286             nan     0.0500   -0.0274
##    140        2.1165             nan     0.0500   -0.0093
##    160        1.9288             nan     0.0500   -0.0171
##    180        1.7452             nan     0.0500   -0.0105
##    200        1.6083             nan     0.0500   -0.0147
##    220        1.4810             nan     0.0500   -0.0068
##    240        1.3548             nan     0.0500   -0.0130
##    260        1.2571             nan     0.0500   -0.0107
##    280        1.1736             nan     0.0500   -0.0046
##    300        1.0998             nan     0.0500   -0.0045
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.6074             nan     0.0500    5.0928
##      2       52.6480             nan     0.0500    4.9163
##      3       48.2102             nan     0.0500    3.6347
##      4       44.2609             nan     0.0500    4.1666
##      5       40.3370             nan     0.0500    3.6530
##      6       37.0098             nan     0.0500    3.0094
##      7       34.0725             nan     0.0500    3.0988
##      8       31.3034             nan     0.0500    2.5954
##      9       28.7670             nan     0.0500    2.3194
##     10       26.5441             nan     0.0500    2.1188
##     20       12.7643             nan     0.0500    0.7245
##     40        5.2802             nan     0.0500    0.0658
##     60        3.7291             nan     0.0500    0.0021
##     80        3.2486             nan     0.0500   -0.0042
##    100        2.9757             nan     0.0500   -0.0204
##    120        2.7167             nan     0.0500   -0.0158
##    140        2.5299             nan     0.0500   -0.0166
##    160        2.3685             nan     0.0500   -0.0202
##    180        2.2391             nan     0.0500   -0.0166
##    200        2.1204             nan     0.0500   -0.0182
##    220        1.9995             nan     0.0500   -0.0183
##    240        1.8869             nan     0.0500   -0.0239
##    260        1.7872             nan     0.0500   -0.0191
##    280        1.7016             nan     0.0500   -0.0199
##    300        1.6129             nan     0.0500   -0.0161
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.3179             nan     0.0500    5.0482
##      2       52.6618             nan     0.0500    4.7157
##      3       48.3907             nan     0.0500    4.2879
##      4       44.5964             nan     0.0500    4.0063
##      5       41.2366             nan     0.0500    3.9352
##      6       37.9516             nan     0.0500    3.0408
##      7       34.9277             nan     0.0500    2.7338
##      8       32.2526             nan     0.0500    2.7502
##      9       29.7876             nan     0.0500    2.3907
##     10       27.4730             nan     0.0500    2.3297
##     20       13.5299             nan     0.0500    0.7158
##     40        5.6291             nan     0.0500    0.0923
##     60        4.2428             nan     0.0500    0.0166
##     80        3.7190             nan     0.0500   -0.0266
##    100        3.4311             nan     0.0500   -0.0135
##    120        3.2119             nan     0.0500   -0.0191
##    140        3.0013             nan     0.0500   -0.0179
##    160        2.8499             nan     0.0500   -0.0149
##    180        2.6918             nan     0.0500   -0.0134
##    200        2.5854             nan     0.0500   -0.0091
##    220        2.4537             nan     0.0500   -0.0169
##    240        2.3694             nan     0.0500   -0.0126
##    260        2.2707             nan     0.0500   -0.0095
##    280        2.1794             nan     0.0500   -0.0179
##    300        2.0977             nan     0.0500   -0.0161
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.0555             nan     0.1000    7.6138
##      2       49.4053             nan     0.1000    6.6276
##      3       44.3756             nan     0.1000    4.9929
##      4       39.5129             nan     0.1000    4.2009
##      5       35.6855             nan     0.1000    3.3459
##      6       31.8681             nan     0.1000    3.3389
##      7       29.1277             nan     0.1000    2.3886
##      8       26.5008             nan     0.1000    2.8212
##      9       24.3133             nan     0.1000    2.3781
##     10       22.4860             nan     0.1000    1.9166
##     20       11.6166             nan     0.1000    0.4115
##     40        5.7238             nan     0.1000    0.0428
##     60        4.5947             nan     0.1000   -0.0107
##     80        4.3573             nan     0.1000   -0.0302
##    100        4.1669             nan     0.1000   -0.0328
##    120        4.0450             nan     0.1000   -0.0275
##    140        3.9471             nan     0.1000   -0.0277
##    160        3.8459             nan     0.1000   -0.0134
##    180        3.7614             nan     0.1000   -0.0474
##    200        3.6922             nan     0.1000   -0.0321
##    220        3.6113             nan     0.1000   -0.0294
##    240        3.5395             nan     0.1000   -0.0268
##    260        3.4978             nan     0.1000   -0.0151
##    280        3.4414             nan     0.1000   -0.0290
##    300        3.3800             nan     0.1000   -0.0228
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.8302             nan     0.1000    7.7743
##      2       49.2898             nan     0.1000    6.5261
##      3       43.6756             nan     0.1000    5.5281
##      4       38.9161             nan     0.1000    4.2792
##      5       35.2476             nan     0.1000    3.5644
##      6       31.9980             nan     0.1000    3.3527
##      7       29.1925             nan     0.1000    2.9808
##      8       26.6720             nan     0.1000    2.2059
##      9       24.4612             nan     0.1000    2.3958
##     10       22.3754             nan     0.1000    1.9165
##     20       11.4983             nan     0.1000    0.5774
##     40        5.7692             nan     0.1000    0.0864
##     60        4.6802             nan     0.1000   -0.0218
##     80        4.3781             nan     0.1000   -0.0262
##    100        4.2141             nan     0.1000   -0.0211
##    120        4.0957             nan     0.1000   -0.0234
##    140        4.0209             nan     0.1000   -0.0118
##    160        3.9366             nan     0.1000   -0.0429
##    180        3.8693             nan     0.1000   -0.0056
##    200        3.8009             nan     0.1000   -0.0598
##    220        3.7237             nan     0.1000   -0.0110
##    240        3.6610             nan     0.1000   -0.0210
##    260        3.5977             nan     0.1000   -0.0242
##    280        3.5416             nan     0.1000   -0.0202
##    300        3.4865             nan     0.1000   -0.0263
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.1560             nan     0.1000    7.5652
##      2       48.7478             nan     0.1000    7.1845
##      3       44.1969             nan     0.1000    5.0202
##      4       39.5508             nan     0.1000    4.1347
##      5       35.6722             nan     0.1000    4.4838
##      6       32.5038             nan     0.1000    3.4300
##      7       29.6769             nan     0.1000    2.8936
##      8       27.1966             nan     0.1000    2.4818
##      9       24.8045             nan     0.1000    2.2810
##     10       23.0550             nan     0.1000    1.4913
##     20       11.6762             nan     0.1000    0.6798
##     40        6.1148             nan     0.1000    0.0010
##     60        5.0670             nan     0.1000    0.0180
##     80        4.8137             nan     0.1000   -0.0086
##    100        4.6319             nan     0.1000   -0.0072
##    120        4.4658             nan     0.1000   -0.0142
##    140        4.3333             nan     0.1000   -0.0153
##    160        4.2019             nan     0.1000   -0.0058
##    180        4.1027             nan     0.1000   -0.0075
##    200        4.0159             nan     0.1000   -0.0067
##    220        3.9518             nan     0.1000   -0.0037
##    240        3.8675             nan     0.1000   -0.0124
##    260        3.8004             nan     0.1000   -0.0308
##    280        3.7408             nan     0.1000   -0.0082
##    300        3.6883             nan     0.1000   -0.0310
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.0963             nan     0.1000    9.8866
##      2       45.0318             nan     0.1000    7.6146
##      3       38.3788             nan     0.1000    6.0393
##      4       32.5579             nan     0.1000    5.8196
##      5       28.3663             nan     0.1000    4.4236
##      6       24.5766             nan     0.1000    3.4699
##      7       21.2377             nan     0.1000    2.9415
##      8       18.5014             nan     0.1000    2.6313
##      9       16.2677             nan     0.1000    2.1613
##     10       14.5832             nan     0.1000    1.6213
##     20        6.1299             nan     0.1000    0.2781
##     40        3.6978             nan     0.1000   -0.0354
##     60        3.1180             nan     0.1000   -0.0488
##     80        2.7482             nan     0.1000   -0.0412
##    100        2.4495             nan     0.1000   -0.0057
##    120        2.2052             nan     0.1000   -0.0500
##    140        2.0275             nan     0.1000   -0.0195
##    160        1.8484             nan     0.1000   -0.0145
##    180        1.6781             nan     0.1000   -0.0089
##    200        1.5636             nan     0.1000   -0.0083
##    220        1.4625             nan     0.1000   -0.0095
##    240        1.3620             nan     0.1000   -0.0195
##    260        1.2561             nan     0.1000   -0.0181
##    280        1.1577             nan     0.1000   -0.0176
##    300        1.0865             nan     0.1000   -0.0142
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.4982             nan     0.1000    9.9704
##      2       45.4958             nan     0.1000    8.1663
##      3       38.8439             nan     0.1000    6.8676
##      4       33.1167             nan     0.1000    5.0847
##      5       28.6498             nan     0.1000    5.0926
##      6       24.9079             nan     0.1000    3.6444
##      7       21.5416             nan     0.1000    3.4117
##      8       18.8026             nan     0.1000    2.5982
##      9       16.4677             nan     0.1000    2.2438
##     10       14.4618             nan     0.1000    1.6878
##     20        6.2608             nan     0.1000    0.1583
##     40        3.9947             nan     0.1000    0.0048
##     60        3.4532             nan     0.1000   -0.0695
##     80        3.0753             nan     0.1000   -0.0239
##    100        2.8015             nan     0.1000   -0.0292
##    120        2.6091             nan     0.1000   -0.0193
##    140        2.4383             nan     0.1000   -0.0359
##    160        2.2716             nan     0.1000   -0.0380
##    180        2.1292             nan     0.1000   -0.0148
##    200        2.0087             nan     0.1000   -0.0186
##    220        1.8971             nan     0.1000   -0.0416
##    240        1.7802             nan     0.1000   -0.0417
##    260        1.6889             nan     0.1000   -0.0367
##    280        1.6110             nan     0.1000   -0.0245
##    300        1.5485             nan     0.1000   -0.0115
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.9706             nan     0.1000    8.4499
##      2       44.8951             nan     0.1000    7.8972
##      3       38.5061             nan     0.1000    6.6447
##      4       32.9478             nan     0.1000    5.5577
##      5       28.5628             nan     0.1000    4.6357
##      6       24.6281             nan     0.1000    4.3533
##      7       21.4555             nan     0.1000    2.9754
##      8       18.7108             nan     0.1000    2.6593
##      9       16.3322             nan     0.1000    2.0587
##     10       14.7445             nan     0.1000    1.3797
##     20        6.5646             nan     0.1000    0.2890
##     40        4.2031             nan     0.1000    0.0166
##     60        3.7148             nan     0.1000   -0.0206
##     80        3.3714             nan     0.1000   -0.0672
##    100        3.1552             nan     0.1000   -0.0106
##    120        2.9588             nan     0.1000   -0.0278
##    140        2.7862             nan     0.1000   -0.0357
##    160        2.6385             nan     0.1000   -0.0402
##    180        2.4985             nan     0.1000   -0.0109
##    200        2.3784             nan     0.1000   -0.0173
##    220        2.2683             nan     0.1000   -0.0245
##    240        2.1426             nan     0.1000   -0.0382
##    260        2.0659             nan     0.1000   -0.0299
##    280        1.9689             nan     0.1000   -0.0278
##    300        1.8860             nan     0.1000   -0.0281
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.4231             nan     0.1000   10.4776
##      2       43.6174             nan     0.1000    8.8853
##      3       36.5281             nan     0.1000    6.8172
##      4       30.3330             nan     0.1000    6.1187
##      5       25.7279             nan     0.1000    4.5879
##      6       21.7777             nan     0.1000    3.9557
##      7       18.6932             nan     0.1000    2.8688
##      8       16.2063             nan     0.1000    2.3279
##      9       14.1763             nan     0.1000    2.1501
##     10       12.4376             nan     0.1000    1.5747
##     20        5.0274             nan     0.1000    0.1613
##     40        2.9364             nan     0.1000   -0.0381
##     60        2.3085             nan     0.1000   -0.0215
##     80        1.9718             nan     0.1000   -0.0501
##    100        1.6574             nan     0.1000   -0.0200
##    120        1.4683             nan     0.1000   -0.0246
##    140        1.2600             nan     0.1000   -0.0235
##    160        1.1154             nan     0.1000   -0.0274
##    180        0.9772             nan     0.1000   -0.0108
##    200        0.8796             nan     0.1000   -0.0109
##    220        0.7815             nan     0.1000   -0.0122
##    240        0.6943             nan     0.1000   -0.0106
##    260        0.6313             nan     0.1000   -0.0111
##    280        0.5660             nan     0.1000   -0.0107
##    300        0.5065             nan     0.1000   -0.0150
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.4970             nan     0.1000   10.8697
##      2       44.0576             nan     0.1000    9.3068
##      3       36.8649             nan     0.1000    6.4076
##      4       31.0434             nan     0.1000    5.0661
##      5       26.1874             nan     0.1000    4.9966
##      6       22.3797             nan     0.1000    3.8592
##      7       19.1280             nan     0.1000    2.8584
##      8       16.6271             nan     0.1000    2.2757
##      9       14.4346             nan     0.1000    2.4008
##     10       12.5175             nan     0.1000    1.8723
##     20        5.1001             nan     0.1000    0.1752
##     40        3.2941             nan     0.1000   -0.0273
##     60        2.7400             nan     0.1000   -0.0285
##     80        2.4257             nan     0.1000   -0.0495
##    100        2.1519             nan     0.1000   -0.0354
##    120        1.9314             nan     0.1000   -0.0213
##    140        1.7634             nan     0.1000   -0.0355
##    160        1.6108             nan     0.1000   -0.0276
##    180        1.4476             nan     0.1000   -0.0052
##    200        1.3215             nan     0.1000   -0.0201
##    220        1.2010             nan     0.1000   -0.0115
##    240        1.1009             nan     0.1000   -0.0314
##    260        1.0174             nan     0.1000   -0.0207
##    280        0.9310             nan     0.1000   -0.0099
##    300        0.8544             nan     0.1000   -0.0157
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.8931             nan     0.1000   10.7929
##      2       44.0418             nan     0.1000    8.6762
##      3       37.1766             nan     0.1000    6.8882
##      4       31.2344             nan     0.1000    6.0162
##      5       26.8118             nan     0.1000    4.1608
##      6       22.8499             nan     0.1000    3.4959
##      7       19.5087             nan     0.1000    3.1667
##      8       16.7145             nan     0.1000    2.4238
##      9       14.4300             nan     0.1000    1.8385
##     10       12.6616             nan     0.1000    1.7262
##     20        5.6587             nan     0.1000    0.1290
##     40        3.6989             nan     0.1000   -0.0200
##     60        3.2331             nan     0.1000   -0.0273
##     80        2.8993             nan     0.1000   -0.0444
##    100        2.6459             nan     0.1000   -0.0500
##    120        2.4334             nan     0.1000   -0.0359
##    140        2.2422             nan     0.1000   -0.0146
##    160        2.1009             nan     0.1000   -0.0271
##    180        1.9614             nan     0.1000   -0.0454
##    200        1.8339             nan     0.1000   -0.0387
##    220        1.7056             nan     0.1000   -0.0175
##    240        1.6171             nan     0.1000   -0.0167
##    260        1.5325             nan     0.1000   -0.0261
##    280        1.4494             nan     0.1000   -0.0204
##    300        1.3485             nan     0.1000   -0.0152
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.0136             nan     0.0100    0.7309
##      2       59.3069             nan     0.0100    0.7382
##      3       58.5576             nan     0.0100    0.7789
##      4       57.8037             nan     0.0100    0.7113
##      5       57.0877             nan     0.0100    0.7058
##      6       56.4809             nan     0.0100    0.6428
##      7       55.7992             nan     0.0100    0.6837
##      8       55.1057             nan     0.0100    0.6259
##      9       54.4941             nan     0.0100    0.6791
##     10       53.8237             nan     0.0100    0.6243
##     20       47.9664             nan     0.0100    0.5467
##     40       38.8344             nan     0.0100    0.3942
##     60       31.6753             nan     0.0100    0.2936
##     80       26.3466             nan     0.0100    0.2201
##    100       22.3660             nan     0.0100    0.1732
##    120       19.1525             nan     0.0100    0.1382
##    140       16.6830             nan     0.0100    0.0901
##    160       14.6963             nan     0.0100    0.0965
##    180       13.0591             nan     0.0100    0.0644
##    200       11.6676             nan     0.0100    0.0577
##    220       10.5019             nan     0.0100    0.0409
##    240        9.5397             nan     0.0100    0.0406
##    260        8.7367             nan     0.0100    0.0311
##    280        8.0795             nan     0.0100    0.0274
##    300        7.5040             nan     0.0100    0.0239
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.9436             nan     0.0100    0.7622
##      2       59.2310             nan     0.0100    0.7166
##      3       58.5180             nan     0.0100    0.7284
##      4       57.7595             nan     0.0100    0.7294
##      5       57.0582             nan     0.0100    0.7101
##      6       56.3601             nan     0.0100    0.6717
##      7       55.6781             nan     0.0100    0.6456
##      8       55.0569             nan     0.0100    0.6686
##      9       54.3782             nan     0.0100    0.6809
##     10       53.6954             nan     0.0100    0.6122
##     20       47.9674             nan     0.0100    0.4422
##     40       38.5254             nan     0.0100    0.4304
##     60       31.7045             nan     0.0100    0.3010
##     80       26.4296             nan     0.0100    0.2286
##    100       22.2645             nan     0.0100    0.1682
##    120       19.1053             nan     0.0100    0.1409
##    140       16.5966             nan     0.0100    0.1006
##    160       14.5532             nan     0.0100    0.0828
##    180       12.9567             nan     0.0100    0.0750
##    200       11.5875             nan     0.0100    0.0470
##    220       10.4623             nan     0.0100    0.0404
##    240        9.5353             nan     0.0100    0.0332
##    260        8.7254             nan     0.0100    0.0305
##    280        8.0461             nan     0.0100    0.0207
##    300        7.4677             nan     0.0100    0.0259
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.0122             nan     0.0100    0.7683
##      2       59.2449             nan     0.0100    0.6982
##      3       58.5086             nan     0.0100    0.6877
##      4       57.8015             nan     0.0100    0.7082
##      5       57.0913             nan     0.0100    0.6731
##      6       56.3931             nan     0.0100    0.6994
##      7       55.6996             nan     0.0100    0.6350
##      8       54.9988             nan     0.0100    0.6720
##      9       54.3109             nan     0.0100    0.6354
##     10       53.7220             nan     0.0100    0.6084
##     20       47.7270             nan     0.0100    0.5410
##     40       38.4607             nan     0.0100    0.3610
##     60       31.3869             nan     0.0100    0.2680
##     80       26.0707             nan     0.0100    0.2191
##    100       21.9812             nan     0.0100    0.1612
##    120       18.8080             nan     0.0100    0.1231
##    140       16.3560             nan     0.0100    0.0885
##    160       14.4122             nan     0.0100    0.0688
##    180       12.8133             nan     0.0100    0.0640
##    200       11.5218             nan     0.0100    0.0530
##    220       10.4511             nan     0.0100    0.0348
##    240        9.5459             nan     0.0100    0.0383
##    260        8.7682             nan     0.0100    0.0243
##    280        8.0921             nan     0.0100    0.0221
##    300        7.5235             nan     0.0100    0.0240
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.7489             nan     0.0100    1.0797
##      2       58.7624             nan     0.0100    0.9633
##      3       57.8081             nan     0.0100    0.9259
##      4       56.8097             nan     0.0100    0.9561
##      5       55.8406             nan     0.0100    0.9998
##      6       54.8998             nan     0.0100    0.9254
##      7       54.0152             nan     0.0100    0.8577
##      8       53.1138             nan     0.0100    0.8383
##      9       52.2625             nan     0.0100    0.8395
##     10       51.4682             nan     0.0100    0.8769
##     20       43.8430             nan     0.0100    0.6853
##     40       32.4891             nan     0.0100    0.4373
##     60       24.3596             nan     0.0100    0.3014
##     80       18.7091             nan     0.0100    0.2070
##    100       14.7317             nan     0.0100    0.1750
##    120       11.8547             nan     0.0100    0.1179
##    140        9.7365             nan     0.0100    0.0670
##    160        8.1844             nan     0.0100    0.0504
##    180        7.0230             nan     0.0100    0.0427
##    200        6.1941             nan     0.0100    0.0238
##    220        5.5471             nan     0.0100    0.0225
##    240        5.0435             nan     0.0100    0.0177
##    260        4.6756             nan     0.0100    0.0100
##    280        4.3899             nan     0.0100    0.0063
##    300        4.1639             nan     0.0100    0.0040
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.7124             nan     0.0100    0.8964
##      2       58.7423             nan     0.0100    0.9351
##      3       57.7704             nan     0.0100    0.9712
##      4       56.8275             nan     0.0100    0.9298
##      5       55.9085             nan     0.0100    0.9019
##      6       54.9836             nan     0.0100    0.9378
##      7       54.1080             nan     0.0100    0.9011
##      8       53.2389             nan     0.0100    0.8544
##      9       52.3379             nan     0.0100    0.9102
##     10       51.4581             nan     0.0100    0.8512
##     20       43.8869             nan     0.0100    0.6327
##     40       32.3847             nan     0.0100    0.4783
##     60       24.4073             nan     0.0100    0.2961
##     80       18.7973             nan     0.0100    0.2407
##    100       14.8049             nan     0.0100    0.1603
##    120       11.9158             nan     0.0100    0.1197
##    140        9.8633             nan     0.0100    0.0725
##    160        8.3504             nan     0.0100    0.0476
##    180        7.2150             nan     0.0100    0.0409
##    200        6.3375             nan     0.0100    0.0353
##    220        5.7002             nan     0.0100    0.0194
##    240        5.2224             nan     0.0100    0.0113
##    260        4.8668             nan     0.0100    0.0112
##    280        4.5955             nan     0.0100    0.0077
##    300        4.3983             nan     0.0100    0.0019
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.7504             nan     0.0100    0.9070
##      2       58.7920             nan     0.0100    0.8851
##      3       57.8570             nan     0.0100    0.9714
##      4       56.9265             nan     0.0100    0.9617
##      5       56.0434             nan     0.0100    0.9323
##      6       55.1533             nan     0.0100    0.9494
##      7       54.3481             nan     0.0100    0.9272
##      8       53.4239             nan     0.0100    0.8740
##      9       52.5853             nan     0.0100    0.8910
##     10       51.7667             nan     0.0100    0.8426
##     20       44.1633             nan     0.0100    0.7738
##     40       32.4893             nan     0.0100    0.4694
##     60       24.5213             nan     0.0100    0.3394
##     80       18.9678             nan     0.0100    0.2050
##    100       14.9668             nan     0.0100    0.1445
##    120       12.1689             nan     0.0100    0.1153
##    140       10.0862             nan     0.0100    0.0811
##    160        8.5776             nan     0.0100    0.0544
##    180        7.4089             nan     0.0100    0.0373
##    200        6.5669             nan     0.0100    0.0363
##    220        5.9359             nan     0.0100    0.0219
##    240        5.4538             nan     0.0100    0.0095
##    260        5.0953             nan     0.0100    0.0062
##    280        4.8104             nan     0.0100    0.0027
##    300        4.6044             nan     0.0100   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.7258             nan     0.0100    1.0260
##      2       58.6768             nan     0.0100    1.0375
##      3       57.6881             nan     0.0100    0.9698
##      4       56.6849             nan     0.0100    1.0019
##      5       55.6817             nan     0.0100    1.0072
##      6       54.7410             nan     0.0100    1.0024
##      7       53.7879             nan     0.0100    0.9439
##      8       52.8675             nan     0.0100    0.9711
##      9       51.9600             nan     0.0100    0.8284
##     10       51.0695             nan     0.0100    0.8600
##     20       43.0478             nan     0.0100    0.6777
##     40       30.9084             nan     0.0100    0.4363
##     60       22.6216             nan     0.0100    0.2841
##     80       16.9050             nan     0.0100    0.2333
##    100       12.9873             nan     0.0100    0.1665
##    120       10.2681             nan     0.0100    0.1101
##    140        8.2704             nan     0.0100    0.0788
##    160        6.8515             nan     0.0100    0.0613
##    180        5.8686             nan     0.0100    0.0225
##    200        5.1360             nan     0.0100    0.0168
##    220        4.6025             nan     0.0100    0.0188
##    240        4.2339             nan     0.0100    0.0094
##    260        3.9213             nan     0.0100    0.0007
##    280        3.6690             nan     0.0100    0.0025
##    300        3.4709             nan     0.0100   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.6912             nan     0.0100    1.1606
##      2       58.6333             nan     0.0100    0.9028
##      3       57.6481             nan     0.0100    0.9227
##      4       56.6834             nan     0.0100    0.9183
##      5       55.7213             nan     0.0100    0.9961
##      6       54.7459             nan     0.0100    0.9148
##      7       53.8130             nan     0.0100    0.8938
##      8       52.8851             nan     0.0100    0.8005
##      9       51.9532             nan     0.0100    0.8151
##     10       51.0693             nan     0.0100    0.8338
##     20       42.9955             nan     0.0100    0.6778
##     40       31.0907             nan     0.0100    0.4672
##     60       22.7488             nan     0.0100    0.3630
##     80       17.0070             nan     0.0100    0.2167
##    100       13.1385             nan     0.0100    0.1596
##    120       10.3236             nan     0.0100    0.1102
##    140        8.4081             nan     0.0100    0.0779
##    160        7.0312             nan     0.0100    0.0479
##    180        6.0379             nan     0.0100    0.0314
##    200        5.3245             nan     0.0100    0.0155
##    220        4.7935             nan     0.0100    0.0173
##    240        4.4352             nan     0.0100    0.0070
##    260        4.1571             nan     0.0100    0.0076
##    280        3.9369             nan     0.0100    0.0057
##    300        3.7738             nan     0.0100    0.0021
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.7164             nan     0.0100    1.0220
##      2       58.7366             nan     0.0100    0.9292
##      3       57.7222             nan     0.0100    0.9462
##      4       56.6980             nan     0.0100    0.9495
##      5       55.7850             nan     0.0100    0.9422
##      6       54.8432             nan     0.0100    0.8777
##      7       53.9257             nan     0.0100    1.0183
##      8       52.9730             nan     0.0100    0.9172
##      9       52.0858             nan     0.0100    0.8241
##     10       51.1973             nan     0.0100    0.9727
##     20       43.2499             nan     0.0100    0.6815
##     40       31.2662             nan     0.0100    0.4994
##     60       23.0804             nan     0.0100    0.3214
##     80       17.3888             nan     0.0100    0.2367
##    100       13.4122             nan     0.0100    0.1580
##    120       10.6160             nan     0.0100    0.1278
##    140        8.7383             nan     0.0100    0.0649
##    160        7.3541             nan     0.0100    0.0549
##    180        6.3414             nan     0.0100    0.0258
##    200        5.6619             nan     0.0100    0.0221
##    220        5.1349             nan     0.0100    0.0153
##    240        4.7506             nan     0.0100    0.0090
##    260        4.4798             nan     0.0100    0.0064
##    280        4.2722             nan     0.0100    0.0031
##    300        4.1111             nan     0.0100    0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.0708             nan     0.0500    3.4261
##      2       53.4832             nan     0.0500    3.5470
##      3       50.2492             nan     0.0500    2.9251
##      4       47.4588             nan     0.0500    2.7320
##      5       44.6253             nan     0.0500    2.3892
##      6       42.0264             nan     0.0500    2.2837
##      7       39.6164             nan     0.0500    2.2924
##      8       37.5523             nan     0.0500    1.8032
##      9       35.5230             nan     0.0500    1.7495
##     10       33.7832             nan     0.0500    1.7217
##     20       21.4582             nan     0.0500    0.7238
##     40       11.1680             nan     0.0500    0.3059
##     60        7.2943             nan     0.0500    0.0969
##     80        5.5371             nan     0.0500    0.0298
##    100        4.7182             nan     0.0500    0.0174
##    120        4.2944             nan     0.0500   -0.0006
##    140        4.0854             nan     0.0500    0.0033
##    160        3.9731             nan     0.0500    0.0011
##    180        3.9094             nan     0.0500   -0.0373
##    200        3.8431             nan     0.0500    0.0023
##    220        3.7720             nan     0.0500   -0.0119
##    240        3.7190             nan     0.0500   -0.0093
##    260        3.6729             nan     0.0500   -0.0129
##    280        3.6199             nan     0.0500   -0.0257
##    300        3.5688             nan     0.0500   -0.0191
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.9279             nan     0.0500    3.5968
##      2       53.2888             nan     0.0500    3.3480
##      3       50.0576             nan     0.0500    2.6050
##      4       47.4279             nan     0.0500    2.7749
##      5       44.9536             nan     0.0500    2.2471
##      6       42.6819             nan     0.0500    2.3080
##      7       40.6400             nan     0.0500    2.2747
##      8       38.3433             nan     0.0500    2.1405
##      9       36.4497             nan     0.0500    1.8345
##     10       34.6595             nan     0.0500    1.7560
##     20       21.9080             nan     0.0500    0.8333
##     40       11.3024             nan     0.0500    0.2667
##     60        7.3082             nan     0.0500    0.0769
##     80        5.5560             nan     0.0500    0.0589
##    100        4.7648             nan     0.0500    0.0268
##    120        4.3887             nan     0.0500   -0.0072
##    140        4.2133             nan     0.0500   -0.0085
##    160        4.0883             nan     0.0500   -0.0011
##    180        3.9976             nan     0.0500   -0.0048
##    200        3.9324             nan     0.0500   -0.0069
##    220        3.8944             nan     0.0500   -0.0134
##    240        3.8400             nan     0.0500   -0.0030
##    260        3.7719             nan     0.0500   -0.0172
##    280        3.7233             nan     0.0500   -0.0166
##    300        3.6749             nan     0.0500   -0.0062
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.8978             nan     0.0500    3.7894
##      2       53.5460             nan     0.0500    3.3779
##      3       50.9201             nan     0.0500    2.5757
##      4       48.0603             nan     0.0500    2.7026
##      5       45.2111             nan     0.0500    2.7884
##      6       42.6579             nan     0.0500    2.0706
##      7       40.2014             nan     0.0500    2.2035
##      8       38.0148             nan     0.0500    1.9890
##      9       36.1214             nan     0.0500    1.8842
##     10       34.1316             nan     0.0500    1.7787
##     20       21.6834             nan     0.0500    0.9314
##     40       11.1737             nan     0.0500    0.2354
##     60        7.2628             nan     0.0500    0.0347
##     80        5.5891             nan     0.0500    0.0537
##    100        4.8670             nan     0.0500    0.0175
##    120        4.5663             nan     0.0500   -0.0026
##    140        4.3998             nan     0.0500    0.0058
##    160        4.2818             nan     0.0500   -0.0066
##    180        4.2154             nan     0.0500   -0.0180
##    200        4.1390             nan     0.0500   -0.0084
##    220        4.0767             nan     0.0500   -0.0062
##    240        4.0111             nan     0.0500   -0.0099
##    260        3.9545             nan     0.0500   -0.0106
##    280        3.9060             nan     0.0500   -0.0047
##    300        3.8599             nan     0.0500   -0.0144
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.9425             nan     0.0500    4.4165
##      2       51.6150             nan     0.0500    4.2477
##      3       47.5709             nan     0.0500    3.8185
##      4       44.0024             nan     0.0500    3.5616
##      5       40.4063             nan     0.0500    3.2855
##      6       37.4479             nan     0.0500    2.9129
##      7       34.7746             nan     0.0500    2.9527
##      8       32.3206             nan     0.0500    2.2662
##      9       29.9910             nan     0.0500    2.2748
##     10       27.7969             nan     0.0500    2.3176
##     20       14.6070             nan     0.0500    0.7513
##     40        6.2589             nan     0.0500    0.0918
##     60        4.1484             nan     0.0500    0.0122
##     80        3.5259             nan     0.0500   -0.0068
##    100        3.1920             nan     0.0500   -0.0192
##    120        2.9503             nan     0.0500   -0.0159
##    140        2.7679             nan     0.0500   -0.0069
##    160        2.5788             nan     0.0500   -0.0014
##    180        2.4560             nan     0.0500   -0.0029
##    200        2.3124             nan     0.0500   -0.0062
##    220        2.1909             nan     0.0500   -0.0118
##    240        2.1008             nan     0.0500   -0.0078
##    260        2.0215             nan     0.0500   -0.0095
##    280        1.9458             nan     0.0500   -0.0138
##    300        1.8626             nan     0.0500   -0.0176
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.9850             nan     0.0500    4.9956
##      2       51.5332             nan     0.0500    4.4692
##      3       47.4913             nan     0.0500    4.3128
##      4       43.8275             nan     0.0500    3.6534
##      5       40.5928             nan     0.0500    3.5577
##      6       37.6119             nan     0.0500    3.1624
##      7       34.7157             nan     0.0500    2.4115
##      8       32.0531             nan     0.0500    2.4976
##      9       29.7861             nan     0.0500    2.2356
##     10       27.6384             nan     0.0500    1.8172
##     20       14.7108             nan     0.0500    0.6873
##     40        6.5437             nan     0.0500    0.1095
##     60        4.5010             nan     0.0500    0.0318
##     80        3.9269             nan     0.0500   -0.0094
##    100        3.5592             nan     0.0500   -0.0372
##    120        3.3482             nan     0.0500   -0.0346
##    140        3.1675             nan     0.0500   -0.0130
##    160        3.0351             nan     0.0500   -0.0165
##    180        2.9170             nan     0.0500   -0.0175
##    200        2.7937             nan     0.0500   -0.0070
##    220        2.6939             nan     0.0500   -0.0158
##    240        2.5863             nan     0.0500   -0.0158
##    260        2.4855             nan     0.0500   -0.0146
##    280        2.3889             nan     0.0500   -0.0096
##    300        2.3010             nan     0.0500   -0.0093
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.7401             nan     0.0500    4.9635
##      2       51.3754             nan     0.0500    4.7747
##      3       47.3313             nan     0.0500    4.1007
##      4       43.5089             nan     0.0500    3.7418
##      5       39.8664             nan     0.0500    3.4419
##      6       36.7988             nan     0.0500    2.9628
##      7       33.9349             nan     0.0500    2.6719
##      8       31.3412             nan     0.0500    2.4528
##      9       29.0799             nan     0.0500    2.1439
##     10       27.0874             nan     0.0500    2.1597
##     20       14.5006             nan     0.0500    0.8585
##     40        6.5885             nan     0.0500    0.1377
##     60        4.6493             nan     0.0500   -0.0094
##     80        4.1056             nan     0.0500   -0.0054
##    100        3.8363             nan     0.0500   -0.0279
##    120        3.6283             nan     0.0500   -0.0136
##    140        3.4383             nan     0.0500   -0.0174
##    160        3.3067             nan     0.0500   -0.0139
##    180        3.1766             nan     0.0500   -0.0075
##    200        3.0857             nan     0.0500   -0.0393
##    220        2.9728             nan     0.0500   -0.0330
##    240        2.8943             nan     0.0500   -0.0329
##    260        2.8106             nan     0.0500   -0.0060
##    280        2.7267             nan     0.0500   -0.0197
##    300        2.6547             nan     0.0500   -0.0091
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.5695             nan     0.0500    4.8853
##      2       51.0947             nan     0.0500    4.5775
##      3       46.7267             nan     0.0500    4.6384
##      4       42.8285             nan     0.0500    3.5816
##      5       39.3509             nan     0.0500    3.3451
##      6       36.1016             nan     0.0500    2.9419
##      7       33.0778             nan     0.0500    2.7151
##      8       30.4066             nan     0.0500    2.6578
##      9       28.1277             nan     0.0500    1.8531
##     10       26.0370             nan     0.0500    2.1431
##     20       12.7724             nan     0.0500    0.6495
##     40        5.1641             nan     0.0500    0.0681
##     60        3.4869             nan     0.0500    0.0085
##     80        2.8689             nan     0.0500   -0.0127
##    100        2.5049             nan     0.0500   -0.0189
##    120        2.2382             nan     0.0500   -0.0128
##    140        2.0354             nan     0.0500   -0.0146
##    160        1.8743             nan     0.0500   -0.0186
##    180        1.7375             nan     0.0500   -0.0200
##    200        1.5886             nan     0.0500   -0.0122
##    220        1.4566             nan     0.0500   -0.0089
##    240        1.3520             nan     0.0500   -0.0075
##    260        1.2580             nan     0.0500   -0.0072
##    280        1.1705             nan     0.0500   -0.0143
##    300        1.0886             nan     0.0500   -0.0108
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.7056             nan     0.0500    5.2085
##      2       50.8091             nan     0.0500    4.4483
##      3       46.6120             nan     0.0500    4.1828
##      4       42.7302             nan     0.0500    4.0813
##      5       39.3565             nan     0.0500    2.9791
##      6       36.1900             nan     0.0500    2.8864
##      7       33.2310             nan     0.0500    2.5093
##      8       30.7194             nan     0.0500    2.3333
##      9       28.3601             nan     0.0500    2.2850
##     10       26.2464             nan     0.0500    2.1626
##     20       13.1996             nan     0.0500    0.7463
##     40        5.4008             nan     0.0500    0.0814
##     60        3.8696             nan     0.0500   -0.0022
##     80        3.3145             nan     0.0500   -0.0160
##    100        3.0309             nan     0.0500   -0.0381
##    120        2.8319             nan     0.0500   -0.0083
##    140        2.6408             nan     0.0500   -0.0087
##    160        2.4663             nan     0.0500   -0.0138
##    180        2.3286             nan     0.0500   -0.0124
##    200        2.1742             nan     0.0500   -0.0277
##    220        2.0430             nan     0.0500   -0.0089
##    240        1.9449             nan     0.0500   -0.0173
##    260        1.8509             nan     0.0500   -0.0158
##    280        1.7439             nan     0.0500   -0.0144
##    300        1.6656             nan     0.0500   -0.0160
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.7311             nan     0.0500    4.9251
##      2       51.1347             nan     0.0500    4.9513
##      3       46.8736             nan     0.0500    4.0580
##      4       43.2034             nan     0.0500    3.8862
##      5       39.5175             nan     0.0500    3.2606
##      6       36.4749             nan     0.0500    3.3231
##      7       33.6722             nan     0.0500    2.7951
##      8       31.1216             nan     0.0500    2.5245
##      9       28.9134             nan     0.0500    2.2811
##     10       26.7782             nan     0.0500    2.0307
##     20       13.2069             nan     0.0500    0.7264
##     40        5.5949             nan     0.0500    0.1197
##     60        4.1884             nan     0.0500   -0.0012
##     80        3.7078             nan     0.0500   -0.0077
##    100        3.4398             nan     0.0500   -0.0144
##    120        3.2301             nan     0.0500   -0.0226
##    140        3.0666             nan     0.0500   -0.0096
##    160        2.8969             nan     0.0500   -0.0150
##    180        2.7509             nan     0.0500   -0.0088
##    200        2.6578             nan     0.0500   -0.0292
##    220        2.5397             nan     0.0500   -0.0141
##    240        2.4359             nan     0.0500   -0.0224
##    260        2.3378             nan     0.0500   -0.0136
##    280        2.2502             nan     0.0500   -0.0277
##    300        2.1721             nan     0.0500   -0.0121
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.0201             nan     0.1000    6.7912
##      2       47.1671             nan     0.1000    5.6943
##      3       41.8649             nan     0.1000    4.6413
##      4       37.3717             nan     0.1000    3.9270
##      5       33.4679             nan     0.1000    3.7025
##      6       30.7213             nan     0.1000    2.6611
##      7       27.9469             nan     0.1000    2.6493
##      8       25.6977             nan     0.1000    2.1255
##      9       23.1097             nan     0.1000    1.9221
##     10       21.0413             nan     0.1000    1.6492
##     20       11.1598             nan     0.1000    0.4762
##     40        5.4967             nan     0.1000    0.0876
##     60        4.3596             nan     0.1000   -0.0277
##     80        4.0585             nan     0.1000   -0.0126
##    100        3.9124             nan     0.1000   -0.0106
##    120        3.8047             nan     0.1000   -0.0418
##    140        3.6896             nan     0.1000   -0.0394
##    160        3.5624             nan     0.1000   -0.0304
##    180        3.4733             nan     0.1000   -0.0154
##    200        3.4134             nan     0.1000   -0.0360
##    220        3.3151             nan     0.1000    0.0020
##    240        3.2590             nan     0.1000   -0.0441
##    260        3.1976             nan     0.1000   -0.0249
##    280        3.1588             nan     0.1000   -0.0069
##    300        3.0971             nan     0.1000   -0.0161
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.9720             nan     0.1000    7.0820
##      2       47.8281             nan     0.1000    5.5254
##      3       43.0021             nan     0.1000    4.5666
##      4       38.2168             nan     0.1000    4.3922
##      5       33.9648             nan     0.1000    3.4568
##      6       30.6128             nan     0.1000    3.2502
##      7       27.7217             nan     0.1000    2.7963
##      8       25.6289             nan     0.1000    1.8686
##      9       23.4901             nan     0.1000    1.9565
##     10       21.6803             nan     0.1000    1.6614
##     20       11.0081             nan     0.1000    0.4699
##     40        5.6507             nan     0.1000    0.1134
##     60        4.5026             nan     0.1000    0.0090
##     80        4.2631             nan     0.1000   -0.0388
##    100        4.0980             nan     0.1000   -0.0167
##    120        3.9801             nan     0.1000   -0.0244
##    140        3.8476             nan     0.1000   -0.0189
##    160        3.7643             nan     0.1000   -0.0034
##    180        3.6628             nan     0.1000   -0.0406
##    200        3.5835             nan     0.1000   -0.0117
##    220        3.5044             nan     0.1000   -0.0109
##    240        3.4446             nan     0.1000   -0.0361
##    260        3.3838             nan     0.1000   -0.0078
##    280        3.3332             nan     0.1000   -0.0151
##    300        3.2810             nan     0.1000   -0.0139
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.2314             nan     0.1000    5.7575
##      2       47.3634             nan     0.1000    5.7790
##      3       41.9419             nan     0.1000    5.3284
##      4       37.8054             nan     0.1000    4.3571
##      5       33.7300             nan     0.1000    3.0227
##      6       30.5260             nan     0.1000    3.4160
##      7       27.9784             nan     0.1000    2.5033
##      8       25.6028             nan     0.1000    2.3520
##      9       23.8093             nan     0.1000    1.6925
##     10       21.9359             nan     0.1000    1.8866
##     20       11.3268             nan     0.1000    0.6500
##     40        5.6763             nan     0.1000    0.1065
##     60        4.6272             nan     0.1000    0.0288
##     80        4.3596             nan     0.1000   -0.0253
##    100        4.1986             nan     0.1000   -0.0032
##    120        4.0725             nan     0.1000   -0.0292
##    140        3.9706             nan     0.1000   -0.0367
##    160        3.9017             nan     0.1000   -0.0261
##    180        3.8187             nan     0.1000   -0.0203
##    200        3.7687             nan     0.1000   -0.0258
##    220        3.6867             nan     0.1000    0.0019
##    240        3.6335             nan     0.1000   -0.0391
##    260        3.5575             nan     0.1000   -0.0036
##    280        3.4870             nan     0.1000   -0.0101
##    300        3.4292             nan     0.1000   -0.0392
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.6126             nan     0.1000    9.7468
##      2       43.6817             nan     0.1000    8.1560
##      3       36.8655             nan     0.1000    6.3558
##      4       31.2082             nan     0.1000    4.5009
##      5       26.5961             nan     0.1000    3.7140
##      6       22.8896             nan     0.1000    3.6687
##      7       19.6821             nan     0.1000    2.7012
##      8       17.3127             nan     0.1000    2.0202
##      9       15.3611             nan     0.1000    1.6812
##     10       13.7626             nan     0.1000    1.4007
##     20        6.0800             nan     0.1000    0.2733
##     40        3.6523             nan     0.1000   -0.0383
##     60        3.0679             nan     0.1000   -0.0400
##     80        2.6484             nan     0.1000   -0.0421
##    100        2.3701             nan     0.1000   -0.0294
##    120        2.1359             nan     0.1000   -0.0119
##    140        1.9004             nan     0.1000   -0.0120
##    160        1.7491             nan     0.1000   -0.0176
##    180        1.5818             nan     0.1000   -0.0052
##    200        1.4524             nan     0.1000   -0.0187
##    220        1.3678             nan     0.1000   -0.0142
##    240        1.2733             nan     0.1000   -0.0144
##    260        1.2029             nan     0.1000   -0.0167
##    280        1.1294             nan     0.1000   -0.0155
##    300        1.0542             nan     0.1000   -0.0157
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.2698             nan     0.1000    9.5994
##      2       43.8665             nan     0.1000    7.5947
##      3       37.3628             nan     0.1000    6.3916
##      4       31.9483             nan     0.1000    4.5749
##      5       27.5093             nan     0.1000    4.6154
##      6       24.0422             nan     0.1000    3.1865
##      7       20.9710             nan     0.1000    2.8786
##      8       18.5160             nan     0.1000    1.9100
##      9       16.5711             nan     0.1000    1.7528
##     10       14.8592             nan     0.1000    1.7289
##     20        6.3916             nan     0.1000    0.3117
##     40        3.9074             nan     0.1000   -0.0486
##     60        3.4775             nan     0.1000   -0.0747
##     80        3.1672             nan     0.1000   -0.0339
##    100        2.9344             nan     0.1000   -0.0468
##    120        2.7406             nan     0.1000   -0.0289
##    140        2.5548             nan     0.1000   -0.0593
##    160        2.3731             nan     0.1000   -0.0294
##    180        2.1705             nan     0.1000   -0.0301
##    200        2.0504             nan     0.1000   -0.0356
##    220        1.9382             nan     0.1000   -0.0232
##    240        1.8042             nan     0.1000   -0.0123
##    260        1.6814             nan     0.1000   -0.0280
##    280        1.5893             nan     0.1000   -0.0283
##    300        1.5040             nan     0.1000   -0.0090
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.1814             nan     0.1000    9.9841
##      2       43.5471             nan     0.1000    7.9245
##      3       37.1071             nan     0.1000    6.3505
##      4       31.8947             nan     0.1000    5.1912
##      5       27.5006             nan     0.1000    4.5199
##      6       23.8010             nan     0.1000    3.1701
##      7       20.7672             nan     0.1000    2.7958
##      8       18.4720             nan     0.1000    2.0899
##      9       16.3873             nan     0.1000    2.0008
##     10       14.4815             nan     0.1000    1.6456
##     20        6.5498             nan     0.1000    0.3072
##     40        4.1843             nan     0.1000   -0.0065
##     60        3.6697             nan     0.1000   -0.0241
##     80        3.4116             nan     0.1000    0.0037
##    100        3.2172             nan     0.1000   -0.0608
##    120        3.0223             nan     0.1000   -0.0571
##    140        2.8498             nan     0.1000   -0.0301
##    160        2.6892             nan     0.1000   -0.0272
##    180        2.5411             nan     0.1000   -0.0468
##    200        2.3915             nan     0.1000   -0.0205
##    220        2.3031             nan     0.1000   -0.0556
##    240        2.1949             nan     0.1000   -0.0098
##    260        2.0899             nan     0.1000   -0.0178
##    280        1.9812             nan     0.1000   -0.0209
##    300        1.9129             nan     0.1000   -0.0177
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.2967             nan     0.1000   10.4184
##      2       43.0139             nan     0.1000    7.6793
##      3       35.8131             nan     0.1000    6.2183
##      4       30.1520             nan     0.1000    4.5974
##      5       25.4157             nan     0.1000    5.1502
##      6       21.8744             nan     0.1000    3.7899
##      7       18.7071             nan     0.1000    3.1930
##      8       16.1025             nan     0.1000    2.6241
##      9       13.9893             nan     0.1000    1.9805
##     10       12.5347             nan     0.1000    1.5300
##     20        4.9977             nan     0.1000    0.2533
##     40        2.9033             nan     0.1000   -0.0265
##     60        2.2183             nan     0.1000   -0.0443
##     80        1.8877             nan     0.1000   -0.0629
##    100        1.5792             nan     0.1000   -0.0167
##    120        1.3367             nan     0.1000   -0.0291
##    140        1.1469             nan     0.1000   -0.0117
##    160        0.9912             nan     0.1000   -0.0103
##    180        0.8554             nan     0.1000   -0.0120
##    200        0.7589             nan     0.1000   -0.0074
##    220        0.6743             nan     0.1000   -0.0129
##    240        0.6083             nan     0.1000   -0.0085
##    260        0.5374             nan     0.1000   -0.0076
##    280        0.4791             nan     0.1000   -0.0085
##    300        0.4312             nan     0.1000   -0.0073
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.5501             nan     0.1000   10.3366
##      2       42.0344             nan     0.1000    8.3214
##      3       35.0822             nan     0.1000    7.2918
##      4       29.7430             nan     0.1000    5.0071
##      5       25.0099             nan     0.1000    3.8199
##      6       21.2212             nan     0.1000    3.4886
##      7       18.4426             nan     0.1000    2.4005
##      8       16.0871             nan     0.1000    2.1322
##      9       13.9715             nan     0.1000    2.1783
##     10       12.3125             nan     0.1000    1.3296
##     20        5.0131             nan     0.1000    0.1336
##     40        3.2769             nan     0.1000   -0.0559
##     60        2.7738             nan     0.1000   -0.0476
##     80        2.4876             nan     0.1000   -0.0527
##    100        2.2089             nan     0.1000   -0.0375
##    120        1.9899             nan     0.1000   -0.0262
##    140        1.8270             nan     0.1000   -0.0396
##    160        1.6009             nan     0.1000   -0.0525
##    180        1.4766             nan     0.1000   -0.0362
##    200        1.3451             nan     0.1000   -0.0142
##    220        1.2159             nan     0.1000   -0.0174
##    240        1.1201             nan     0.1000   -0.0062
##    260        1.0292             nan     0.1000   -0.0183
##    280        0.9507             nan     0.1000   -0.0123
##    300        0.8717             nan     0.1000   -0.0074
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.4377             nan     0.1000   10.0612
##      2       42.2709             nan     0.1000    8.2933
##      3       35.7439             nan     0.1000    6.9922
##      4       29.9256             nan     0.1000    5.6780
##      5       25.5106             nan     0.1000    3.9175
##      6       21.9648             nan     0.1000    3.3536
##      7       19.2097             nan     0.1000    2.7483
##      8       16.5308             nan     0.1000    2.3399
##      9       14.4251             nan     0.1000    1.9576
##     10       12.7110             nan     0.1000    1.8204
##     20        5.5993             nan     0.1000    0.2558
##     40        3.8591             nan     0.1000   -0.0401
##     60        3.3687             nan     0.1000   -0.0628
##     80        3.0184             nan     0.1000   -0.0439
##    100        2.7602             nan     0.1000   -0.0416
##    120        2.5498             nan     0.1000   -0.0279
##    140        2.3568             nan     0.1000   -0.0183
##    160        2.1967             nan     0.1000   -0.0228
##    180        2.0427             nan     0.1000   -0.0190
##    200        1.9039             nan     0.1000   -0.0362
##    220        1.7758             nan     0.1000   -0.0369
##    240        1.6617             nan     0.1000   -0.0217
##    260        1.5450             nan     0.1000   -0.0273
##    280        1.4692             nan     0.1000   -0.0114
##    300        1.3721             nan     0.1000   -0.0124
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.7802             nan     0.0100    0.7357
##      2       59.0511             nan     0.0100    0.7323
##      3       58.3037             nan     0.0100    0.6932
##      4       57.5961             nan     0.0100    0.6842
##      5       56.9363             nan     0.0100    0.6610
##      6       56.2517             nan     0.0100    0.6939
##      7       55.6626             nan     0.0100    0.6442
##      8       55.0153             nan     0.0100    0.6660
##      9       54.3390             nan     0.0100    0.6426
##     10       53.6719             nan     0.0100    0.6308
##     20       47.7516             nan     0.0100    0.5090
##     40       38.5617             nan     0.0100    0.3554
##     60       31.9074             nan     0.0100    0.2400
##     80       26.8206             nan     0.0100    0.2481
##    100       22.7279             nan     0.0100    0.1786
##    120       19.5876             nan     0.0100    0.1309
##    140       17.1873             nan     0.0100    0.1063
##    160       15.2162             nan     0.0100    0.0885
##    180       13.6377             nan     0.0100    0.0699
##    200       12.3013             nan     0.0100    0.0474
##    220       11.1336             nan     0.0100    0.0368
##    240       10.2211             nan     0.0100    0.0341
##    260        9.4366             nan     0.0100    0.0278
##    280        8.7483             nan     0.0100    0.0226
##    300        8.1385             nan     0.0100    0.0196
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.8481             nan     0.0100    0.7078
##      2       59.1059             nan     0.0100    0.7472
##      3       58.4287             nan     0.0100    0.7632
##      4       57.7299             nan     0.0100    0.6834
##      5       57.1300             nan     0.0100    0.6425
##      6       56.4520             nan     0.0100    0.6361
##      7       55.7836             nan     0.0100    0.6218
##      8       55.1044             nan     0.0100    0.5973
##      9       54.4765             nan     0.0100    0.6439
##     10       53.8710             nan     0.0100    0.6292
##     20       47.9486             nan     0.0100    0.5116
##     40       38.7935             nan     0.0100    0.3702
##     60       31.9034             nan     0.0100    0.2722
##     80       26.7048             nan     0.0100    0.2088
##    100       22.6756             nan     0.0100    0.1636
##    120       19.5336             nan     0.0100    0.0773
##    140       17.1238             nan     0.0100    0.1133
##    160       15.1701             nan     0.0100    0.0734
##    180       13.5280             nan     0.0100    0.0471
##    200       12.1957             nan     0.0100    0.0429
##    220       11.0711             nan     0.0100    0.0465
##    240       10.1066             nan     0.0100    0.0370
##    260        9.3002             nan     0.0100    0.0371
##    280        8.6126             nan     0.0100    0.0263
##    300        8.0320             nan     0.0100    0.0165
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.8285             nan     0.0100    0.7512
##      2       59.0799             nan     0.0100    0.6581
##      3       58.3778             nan     0.0100    0.7019
##      4       57.6990             nan     0.0100    0.6918
##      5       56.9748             nan     0.0100    0.7173
##      6       56.2622             nan     0.0100    0.6385
##      7       55.5478             nan     0.0100    0.6622
##      8       54.8353             nan     0.0100    0.6592
##      9       54.1843             nan     0.0100    0.6116
##     10       53.5202             nan     0.0100    0.5804
##     20       47.7961             nan     0.0100    0.4823
##     40       38.7859             nan     0.0100    0.4111
##     60       32.0661             nan     0.0100    0.2158
##     80       26.7199             nan     0.0100    0.2148
##    100       22.6224             nan     0.0100    0.1511
##    120       19.5549             nan     0.0100    0.1094
##    140       17.0971             nan     0.0100    0.0986
##    160       15.1495             nan     0.0100    0.0808
##    180       13.5290             nan     0.0100    0.0554
##    200       12.2197             nan     0.0100    0.0438
##    220       11.1515             nan     0.0100    0.0385
##    240       10.2507             nan     0.0100    0.0347
##    260        9.4569             nan     0.0100    0.0268
##    280        8.8141             nan     0.0100    0.0222
##    300        8.2600             nan     0.0100    0.0260
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.5648             nan     0.0100    0.9044
##      2       58.6228             nan     0.0100    0.8239
##      3       57.7422             nan     0.0100    0.8790
##      4       56.8614             nan     0.0100    0.9225
##      5       55.9960             nan     0.0100    0.8283
##      6       55.1309             nan     0.0100    0.8546
##      7       54.1854             nan     0.0100    0.8393
##      8       53.3071             nan     0.0100    0.8552
##      9       52.4641             nan     0.0100    0.8578
##     10       51.6414             nan     0.0100    0.8407
##     20       44.1650             nan     0.0100    0.6280
##     40       32.7486             nan     0.0100    0.4340
##     60       24.6903             nan     0.0100    0.3268
##     80       19.0837             nan     0.0100    0.2384
##    100       15.1189             nan     0.0100    0.1512
##    120       12.2264             nan     0.0100    0.1064
##    140       10.1455             nan     0.0100    0.0819
##    160        8.5828             nan     0.0100    0.0560
##    180        7.3886             nan     0.0100    0.0495
##    200        6.5366             nan     0.0100    0.0363
##    220        5.8293             nan     0.0100    0.0200
##    240        5.3174             nan     0.0100    0.0156
##    260        4.9191             nan     0.0100    0.0101
##    280        4.6012             nan     0.0100    0.0056
##    300        4.3540             nan     0.0100    0.0035
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.5817             nan     0.0100    0.9281
##      2       58.5747             nan     0.0100    0.9947
##      3       57.6325             nan     0.0100    0.9581
##      4       56.7099             nan     0.0100    0.8773
##      5       55.8143             nan     0.0100    0.9412
##      6       54.9267             nan     0.0100    0.8364
##      7       54.0580             nan     0.0100    0.8937
##      8       53.2065             nan     0.0100    0.8718
##      9       52.3735             nan     0.0100    0.8637
##     10       51.5661             nan     0.0100    0.7839
##     20       44.0263             nan     0.0100    0.6910
##     40       32.5168             nan     0.0100    0.4663
##     60       24.6245             nan     0.0100    0.3795
##     80       19.0941             nan     0.0100    0.1941
##    100       15.1076             nan     0.0100    0.1754
##    120       12.2536             nan     0.0100    0.1118
##    140       10.1873             nan     0.0100    0.0859
##    160        8.6745             nan     0.0100    0.0573
##    180        7.4842             nan     0.0100    0.0458
##    200        6.6422             nan     0.0100    0.0350
##    220        5.9885             nan     0.0100    0.0114
##    240        5.4857             nan     0.0100    0.0135
##    260        5.0890             nan     0.0100    0.0083
##    280        4.7837             nan     0.0100    0.0040
##    300        4.5383             nan     0.0100    0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.5857             nan     0.0100    0.9209
##      2       58.5667             nan     0.0100    0.8821
##      3       57.6433             nan     0.0100    0.9076
##      4       56.7130             nan     0.0100    0.8524
##      5       55.7961             nan     0.0100    0.9505
##      6       54.9478             nan     0.0100    0.7982
##      7       54.0999             nan     0.0100    0.9649
##      8       53.2687             nan     0.0100    0.9195
##      9       52.4163             nan     0.0100    0.7728
##     10       51.6273             nan     0.0100    0.8040
##     20       44.0618             nan     0.0100    0.6643
##     40       32.7286             nan     0.0100    0.4396
##     60       25.0251             nan     0.0100    0.3287
##     80       19.4036             nan     0.0100    0.2325
##    100       15.4938             nan     0.0100    0.1422
##    120       12.6591             nan     0.0100    0.0958
##    140       10.6122             nan     0.0100    0.0734
##    160        9.0626             nan     0.0100    0.0541
##    180        7.9023             nan     0.0100    0.0488
##    200        7.0198             nan     0.0100    0.0331
##    220        6.3613             nan     0.0100    0.0293
##    240        5.8480             nan     0.0100    0.0178
##    260        5.4388             nan     0.0100    0.0058
##    280        5.1535             nan     0.0100    0.0098
##    300        4.9236             nan     0.0100    0.0025
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.5101             nan     0.0100    0.9904
##      2       58.4918             nan     0.0100    1.0195
##      3       57.5132             nan     0.0100    0.9932
##      4       56.4853             nan     0.0100    1.0311
##      5       55.5356             nan     0.0100    0.9417
##      6       54.5637             nan     0.0100    0.9455
##      7       53.6098             nan     0.0100    0.9397
##      8       52.7132             nan     0.0100    0.7587
##      9       51.7853             nan     0.0100    0.9676
##     10       50.9311             nan     0.0100    0.7840
##     20       43.1773             nan     0.0100    0.6776
##     40       31.3077             nan     0.0100    0.4155
##     60       23.0982             nan     0.0100    0.2787
##     80       17.2931             nan     0.0100    0.2317
##    100       13.3724             nan     0.0100    0.1454
##    120       10.5697             nan     0.0100    0.1123
##    140        8.5455             nan     0.0100    0.0773
##    160        7.0736             nan     0.0100    0.0459
##    180        6.0550             nan     0.0100    0.0277
##    200        5.2960             nan     0.0100    0.0195
##    220        4.7491             nan     0.0100    0.0189
##    240        4.3263             nan     0.0100    0.0039
##    260        4.0006             nan     0.0100    0.0060
##    280        3.7255             nan     0.0100    0.0021
##    300        3.5140             nan     0.0100    0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.4967             nan     0.0100    1.0480
##      2       58.5022             nan     0.0100    1.0844
##      3       57.4810             nan     0.0100    0.9313
##      4       56.5314             nan     0.0100    0.9639
##      5       55.4949             nan     0.0100    0.9813
##      6       54.5464             nan     0.0100    0.9588
##      7       53.7010             nan     0.0100    0.9853
##      8       52.7920             nan     0.0100    0.8156
##      9       51.8629             nan     0.0100    0.7145
##     10       50.9695             nan     0.0100    0.8524
##     20       43.2519             nan     0.0100    0.7130
##     40       31.1532             nan     0.0100    0.4942
##     60       23.0077             nan     0.0100    0.3057
##     80       17.3690             nan     0.0100    0.2364
##    100       13.4476             nan     0.0100    0.1563
##    120       10.7219             nan     0.0100    0.0936
##    140        8.7297             nan     0.0100    0.0689
##    160        7.3428             nan     0.0100    0.0412
##    180        6.3344             nan     0.0100    0.0357
##    200        5.5801             nan     0.0100    0.0224
##    220        5.0212             nan     0.0100    0.0163
##    240        4.6111             nan     0.0100    0.0061
##    260        4.3105             nan     0.0100   -0.0012
##    280        4.0639             nan     0.0100    0.0045
##    300        3.8846             nan     0.0100   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.5488             nan     0.0100    0.9925
##      2       58.5097             nan     0.0100    0.9841
##      3       57.5452             nan     0.0100    0.8939
##      4       56.5743             nan     0.0100    0.9740
##      5       55.6588             nan     0.0100    0.9509
##      6       54.7102             nan     0.0100    0.8030
##      7       53.7678             nan     0.0100    0.9736
##      8       52.8105             nan     0.0100    0.7952
##      9       51.9219             nan     0.0100    0.8898
##     10       51.0551             nan     0.0100    0.8353
##     20       43.1001             nan     0.0100    0.7881
##     40       31.1791             nan     0.0100    0.4949
##     60       23.1325             nan     0.0100    0.2781
##     80       17.6630             nan     0.0100    0.2352
##    100       13.7272             nan     0.0100    0.1388
##    120       11.0270             nan     0.0100    0.0967
##    140        9.1150             nan     0.0100    0.0683
##    160        7.7501             nan     0.0100    0.0476
##    180        6.7380             nan     0.0100    0.0327
##    200        6.0165             nan     0.0100    0.0292
##    220        5.4903             nan     0.0100    0.0121
##    240        5.1109             nan     0.0100    0.0114
##    260        4.8104             nan     0.0100    0.0025
##    280        4.5825             nan     0.0100    0.0073
##    300        4.4026             nan     0.0100    0.0021
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.7872             nan     0.0500    3.6766
##      2       53.2926             nan     0.0500    3.3236
##      3       50.0704             nan     0.0500    3.0223
##      4       47.6810             nan     0.0500    2.5563
##      5       45.3960             nan     0.0500    2.4589
##      6       42.9093             nan     0.0500    2.6793
##      7       40.5233             nan     0.0500    2.0671
##      8       38.5083             nan     0.0500    1.9015
##      9       36.6530             nan     0.0500    1.7910
##     10       34.8013             nan     0.0500    1.6317
##     20       22.6614             nan     0.0500    0.7718
##     40       12.0838             nan     0.0500    0.2686
##     60        7.9134             nan     0.0500    0.0918
##     80        6.0996             nan     0.0500    0.0308
##    100        5.1189             nan     0.0500    0.0385
##    120        4.6183             nan     0.0500   -0.0121
##    140        4.3756             nan     0.0500   -0.0003
##    160        4.2279             nan     0.0500   -0.0058
##    180        4.1508             nan     0.0500   -0.0153
##    200        4.0516             nan     0.0500   -0.0040
##    220        3.9592             nan     0.0500   -0.0304
##    240        3.9001             nan     0.0500   -0.0211
##    260        3.8370             nan     0.0500   -0.0336
##    280        3.7849             nan     0.0500   -0.0051
##    300        3.7344             nan     0.0500   -0.0206
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.6918             nan     0.0500    3.6445
##      2       53.5021             nan     0.0500    3.2461
##      3       50.2657             nan     0.0500    3.1762
##      4       47.6085             nan     0.0500    2.5090
##      5       44.8983             nan     0.0500    2.5774
##      6       42.6877             nan     0.0500    2.3453
##      7       40.5744             nan     0.0500    2.0587
##      8       38.4281             nan     0.0500    2.1082
##      9       36.3937             nan     0.0500    1.6577
##     10       34.5292             nan     0.0500    1.6177
##     20       22.5464             nan     0.0500    0.8994
##     40       12.1597             nan     0.0500    0.2889
##     60        8.1875             nan     0.0500    0.1153
##     80        6.2349             nan     0.0500    0.0581
##    100        5.3274             nan     0.0500    0.0296
##    120        4.8910             nan     0.0500    0.0066
##    140        4.6459             nan     0.0500    0.0034
##    160        4.4969             nan     0.0500    0.0005
##    180        4.3977             nan     0.0500   -0.0102
##    200        4.3036             nan     0.0500    0.0016
##    220        4.2196             nan     0.0500   -0.0091
##    240        4.1582             nan     0.0500   -0.0180
##    260        4.1127             nan     0.0500   -0.0065
##    280        4.0679             nan     0.0500   -0.0132
##    300        4.0204             nan     0.0500   -0.0115
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.8755             nan     0.0500    3.5794
##      2       53.7424             nan     0.0500    3.2878
##      3       50.6522             nan     0.0500    2.9833
##      4       47.9544             nan     0.0500    2.6395
##      5       45.4138             nan     0.0500    2.7439
##      6       42.8427             nan     0.0500    2.3994
##      7       40.7187             nan     0.0500    1.9552
##      8       38.7492             nan     0.0500    2.1480
##      9       36.9457             nan     0.0500    1.8365
##     10       35.1804             nan     0.0500    1.6570
##     20       22.7692             nan     0.0500    0.8627
##     40       12.1790             nan     0.0500    0.2706
##     60        8.2834             nan     0.0500    0.1099
##     80        6.4187             nan     0.0500    0.0150
##    100        5.5844             nan     0.0500    0.0312
##    120        5.1343             nan     0.0500   -0.0045
##    140        4.9207             nan     0.0500    0.0031
##    160        4.7661             nan     0.0500   -0.0061
##    180        4.6628             nan     0.0500   -0.0155
##    200        4.5723             nan     0.0500    0.0018
##    220        4.4969             nan     0.0500   -0.0043
##    240        4.4312             nan     0.0500   -0.0180
##    260        4.3907             nan     0.0500   -0.0033
##    280        4.3181             nan     0.0500   -0.0060
##    300        4.2693             nan     0.0500   -0.0094
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.5723             nan     0.0500    5.4751
##      2       51.2485             nan     0.0500    5.1950
##      3       47.3208             nan     0.0500    4.0114
##      4       43.6778             nan     0.0500    3.6748
##      5       40.5033             nan     0.0500    3.1818
##      6       37.4376             nan     0.0500    2.7304
##      7       34.6650             nan     0.0500    2.6490
##      8       32.0696             nan     0.0500    2.6748
##      9       29.8378             nan     0.0500    2.3310
##     10       27.8292             nan     0.0500    1.9549
##     20       14.7525             nan     0.0500    0.7964
##     40        6.3952             nan     0.0500    0.1315
##     60        4.3550             nan     0.0500    0.0326
##     80        3.6489             nan     0.0500   -0.0245
##    100        3.2780             nan     0.0500   -0.0102
##    120        3.0935             nan     0.0500   -0.0151
##    140        2.9114             nan     0.0500   -0.0059
##    160        2.7534             nan     0.0500   -0.0015
##    180        2.6077             nan     0.0500   -0.0138
##    200        2.4788             nan     0.0500   -0.0100
##    220        2.3569             nan     0.0500   -0.0035
##    240        2.2370             nan     0.0500   -0.0250
##    260        2.1518             nan     0.0500   -0.0152
##    280        2.0678             nan     0.0500   -0.0143
##    300        1.9833             nan     0.0500   -0.0192
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.4885             nan     0.0500    4.4900
##      2       51.3147             nan     0.0500    4.1321
##      3       47.6127             nan     0.0500    3.8366
##      4       44.0584             nan     0.0500    3.3136
##      5       40.7248             nan     0.0500    3.1548
##      6       37.8650             nan     0.0500    2.8126
##      7       34.9879             nan     0.0500    2.7318
##      8       32.5627             nan     0.0500    2.4684
##      9       30.2428             nan     0.0500    2.3586
##     10       28.2083             nan     0.0500    2.0613
##     20       14.9852             nan     0.0500    0.7585
##     40        6.5936             nan     0.0500    0.1921
##     60        4.5482             nan     0.0500    0.0183
##     80        3.9252             nan     0.0500    0.0021
##    100        3.6024             nan     0.0500   -0.0133
##    120        3.3923             nan     0.0500   -0.0066
##    140        3.2153             nan     0.0500   -0.0156
##    160        3.0899             nan     0.0500   -0.0239
##    180        2.9587             nan     0.0500   -0.0074
##    200        2.8516             nan     0.0500   -0.0246
##    220        2.7438             nan     0.0500   -0.0141
##    240        2.6329             nan     0.0500   -0.0048
##    260        2.5240             nan     0.0500   -0.0081
##    280        2.4396             nan     0.0500   -0.0062
##    300        2.3470             nan     0.0500   -0.0112
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.4685             nan     0.0500    4.4892
##      2       51.1385             nan     0.0500    3.8802
##      3       47.2735             nan     0.0500    3.9465
##      4       43.7178             nan     0.0500    3.0013
##      5       40.2901             nan     0.0500    3.1289
##      6       37.4141             nan     0.0500    2.8831
##      7       34.7332             nan     0.0500    2.7797
##      8       32.2570             nan     0.0500    2.3107
##      9       30.1355             nan     0.0500    2.1410
##     10       28.0738             nan     0.0500    1.8346
##     20       15.4129             nan     0.0500    0.6502
##     40        6.9285             nan     0.0500    0.1711
##     60        4.9471             nan     0.0500    0.0396
##     80        4.3511             nan     0.0500   -0.0267
##    100        4.0525             nan     0.0500   -0.0272
##    120        3.8253             nan     0.0500   -0.0603
##    140        3.6731             nan     0.0500   -0.0169
##    160        3.4932             nan     0.0500   -0.0080
##    180        3.3284             nan     0.0500   -0.0295
##    200        3.2131             nan     0.0500   -0.0124
##    220        3.1113             nan     0.0500   -0.0350
##    240        2.9976             nan     0.0500   -0.0142
##    260        2.8845             nan     0.0500   -0.0079
##    280        2.8048             nan     0.0500   -0.0185
##    300        2.7210             nan     0.0500   -0.0304
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.4433             nan     0.0500    4.6871
##      2       50.6566             nan     0.0500    4.4558
##      3       46.3076             nan     0.0500    4.8334
##      4       42.5614             nan     0.0500    3.4451
##      5       39.3690             nan     0.0500    3.2278
##      6       36.2337             nan     0.0500    2.7395
##      7       33.4289             nan     0.0500    2.6358
##      8       30.8133             nan     0.0500    2.5092
##      9       28.5011             nan     0.0500    2.1957
##     10       26.3029             nan     0.0500    2.0283
##     20       13.2484             nan     0.0500    0.7241
##     40        5.3378             nan     0.0500    0.0719
##     60        3.6090             nan     0.0500    0.0016
##     80        2.9822             nan     0.0500    0.0006
##    100        2.6148             nan     0.0500   -0.0128
##    120        2.3507             nan     0.0500   -0.0235
##    140        2.1540             nan     0.0500   -0.0133
##    160        1.9864             nan     0.0500   -0.0144
##    180        1.8405             nan     0.0500   -0.0140
##    200        1.6762             nan     0.0500   -0.0036
##    220        1.5499             nan     0.0500   -0.0067
##    240        1.4413             nan     0.0500   -0.0130
##    260        1.3428             nan     0.0500   -0.0096
##    280        1.2527             nan     0.0500   -0.0069
##    300        1.1683             nan     0.0500   -0.0093
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.5656             nan     0.0500    4.9245
##      2       50.9659             nan     0.0500    4.5981
##      3       46.7109             nan     0.0500    4.0706
##      4       43.0318             nan     0.0500    3.6478
##      5       39.5982             nan     0.0500    3.5874
##      6       36.2520             nan     0.0500    2.8755
##      7       33.6210             nan     0.0500    2.8678
##      8       31.0300             nan     0.0500    2.3172
##      9       28.6586             nan     0.0500    2.4129
##     10       26.4788             nan     0.0500    2.0020
##     20       13.3763             nan     0.0500    0.8368
##     40        5.4650             nan     0.0500    0.1065
##     60        3.8814             nan     0.0500   -0.0001
##     80        3.3449             nan     0.0500   -0.0239
##    100        2.9858             nan     0.0500   -0.0180
##    120        2.7205             nan     0.0500   -0.0177
##    140        2.5219             nan     0.0500   -0.0300
##    160        2.3445             nan     0.0500   -0.0133
##    180        2.2079             nan     0.0500   -0.0272
##    200        2.0839             nan     0.0500   -0.0065
##    220        1.9516             nan     0.0500   -0.0068
##    240        1.8472             nan     0.0500   -0.0146
##    260        1.7748             nan     0.0500   -0.0011
##    280        1.6891             nan     0.0500   -0.0134
##    300        1.6065             nan     0.0500   -0.0099
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.5143             nan     0.0500    4.9780
##      2       51.0793             nan     0.0500    4.4238
##      3       46.8782             nan     0.0500    3.8999
##      4       42.8993             nan     0.0500    3.9868
##      5       39.6739             nan     0.0500    3.1584
##      6       36.5759             nan     0.0500    2.9466
##      7       33.8833             nan     0.0500    2.6507
##      8       31.1700             nan     0.0500    2.2807
##      9       28.9239             nan     0.0500    2.4934
##     10       26.7299             nan     0.0500    2.2882
##     20       13.5371             nan     0.0500    0.8964
##     40        5.8025             nan     0.0500    0.0847
##     60        4.3024             nan     0.0500    0.0119
##     80        3.8625             nan     0.0500   -0.0205
##    100        3.5652             nan     0.0500   -0.0098
##    120        3.3974             nan     0.0500   -0.0242
##    140        3.1970             nan     0.0500   -0.0252
##    160        3.0094             nan     0.0500   -0.0187
##    180        2.8756             nan     0.0500   -0.0313
##    200        2.7444             nan     0.0500   -0.0184
##    220        2.6012             nan     0.0500   -0.0145
##    240        2.5021             nan     0.0500   -0.0072
##    260        2.4029             nan     0.0500   -0.0191
##    280        2.3015             nan     0.0500   -0.0163
##    300        2.1953             nan     0.0500   -0.0154
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.9176             nan     0.1000    7.0151
##      2       47.9532             nan     0.1000    5.5853
##      3       42.8926             nan     0.1000    4.8187
##      4       38.7388             nan     0.1000    3.8364
##      5       35.3977             nan     0.1000    3.6935
##      6       31.8647             nan     0.1000    3.4015
##      7       28.8960             nan     0.1000    2.6856
##      8       26.3654             nan     0.1000    2.1380
##      9       24.0134             nan     0.1000    1.9854
##     10       21.9709             nan     0.1000    2.0209
##     20       11.6744             nan     0.1000    0.4276
##     40        6.0735             nan     0.1000    0.1003
##     60        4.7100             nan     0.1000    0.0161
##     80        4.2892             nan     0.1000   -0.0251
##    100        4.1021             nan     0.1000   -0.0057
##    120        3.9508             nan     0.1000   -0.0309
##    140        3.8789             nan     0.1000   -0.0226
##    160        3.7843             nan     0.1000   -0.0029
##    180        3.6989             nan     0.1000   -0.0568
##    200        3.6470             nan     0.1000   -0.0236
##    220        3.5851             nan     0.1000   -0.0365
##    240        3.5099             nan     0.1000   -0.0345
##    260        3.4418             nan     0.1000   -0.0083
##    280        3.3784             nan     0.1000   -0.0396
##    300        3.3314             nan     0.1000   -0.0175
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.4171             nan     0.1000    6.6449
##      2       47.8300             nan     0.1000    5.6766
##      3       42.7724             nan     0.1000    3.8795
##      4       37.8536             nan     0.1000    4.4030
##      5       34.0978             nan     0.1000    3.5786
##      6       31.2104             nan     0.1000    2.0190
##      7       28.4670             nan     0.1000    2.4757
##      8       26.0355             nan     0.1000    1.9452
##      9       23.5444             nan     0.1000    2.2629
##     10       21.6720             nan     0.1000    1.8464
##     20       11.5154             nan     0.1000    0.5737
##     40        6.0920             nan     0.1000    0.0317
##     60        4.8708             nan     0.1000   -0.0191
##     80        4.5732             nan     0.1000   -0.0479
##    100        4.3684             nan     0.1000   -0.0290
##    120        4.2346             nan     0.1000   -0.0405
##    140        4.0871             nan     0.1000   -0.0198
##    160        3.9922             nan     0.1000   -0.0035
##    180        3.9139             nan     0.1000   -0.0402
##    200        3.8492             nan     0.1000    0.0051
##    220        3.7670             nan     0.1000   -0.0235
##    240        3.6923             nan     0.1000   -0.0083
##    260        3.6539             nan     0.1000   -0.0194
##    280        3.6077             nan     0.1000   -0.0291
##    300        3.5643             nan     0.1000   -0.0177
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.3800             nan     0.1000    7.0036
##      2       47.3159             nan     0.1000    6.1114
##      3       42.3875             nan     0.1000    5.2513
##      4       38.2181             nan     0.1000    3.8459
##      5       34.5876             nan     0.1000    3.6150
##      6       31.1349             nan     0.1000    3.0653
##      7       28.4608             nan     0.1000    2.6255
##      8       26.1302             nan     0.1000    2.3775
##      9       24.1076             nan     0.1000    1.9873
##     10       22.0113             nan     0.1000    2.1248
##     20       11.5955             nan     0.1000    0.4241
##     40        6.1258             nan     0.1000    0.0968
##     60        5.0537             nan     0.1000   -0.0656
##     80        4.6700             nan     0.1000   -0.0060
##    100        4.4820             nan     0.1000   -0.0005
##    120        4.3614             nan     0.1000   -0.0008
##    140        4.2283             nan     0.1000   -0.0178
##    160        4.1440             nan     0.1000   -0.0267
##    180        4.0592             nan     0.1000   -0.0315
##    200        3.9786             nan     0.1000   -0.0142
##    220        3.9305             nan     0.1000   -0.0163
##    240        3.8593             nan     0.1000   -0.0138
##    260        3.8084             nan     0.1000   -0.0116
##    280        3.7356             nan     0.1000    0.0007
##    300        3.6903             nan     0.1000   -0.0268
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.1298             nan     0.1000    9.2221
##      2       43.5610             nan     0.1000    8.6046
##      3       37.1065             nan     0.1000    6.0624
##      4       32.0674             nan     0.1000    5.0817
##      5       27.6947             nan     0.1000    4.5593
##      6       23.9011             nan     0.1000    3.4295
##      7       20.6683             nan     0.1000    3.1512
##      8       18.1921             nan     0.1000    1.8080
##      9       16.1280             nan     0.1000    2.1147
##     10       14.3391             nan     0.1000    1.4602
##     20        6.3692             nan     0.1000    0.2999
##     40        3.7310             nan     0.1000    0.0111
##     60        3.1778             nan     0.1000   -0.0836
##     80        2.7711             nan     0.1000   -0.0146
##    100        2.4656             nan     0.1000   -0.0200
##    120        2.2339             nan     0.1000   -0.0297
##    140        2.0230             nan     0.1000   -0.0246
##    160        1.8985             nan     0.1000   -0.0155
##    180        1.7470             nan     0.1000   -0.0117
##    200        1.6409             nan     0.1000   -0.0190
##    220        1.5303             nan     0.1000   -0.0177
##    240        1.4277             nan     0.1000   -0.0230
##    260        1.3392             nan     0.1000   -0.0174
##    280        1.2283             nan     0.1000   -0.0142
##    300        1.1600             nan     0.1000   -0.0167
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.9346             nan     0.1000    9.2742
##      2       43.2298             nan     0.1000    7.0547
##      3       36.4575             nan     0.1000    5.9730
##      4       31.0990             nan     0.1000    5.0957
##      5       27.0145             nan     0.1000    3.9160
##      6       23.5909             nan     0.1000    3.4839
##      7       20.4472             nan     0.1000    2.6777
##      8       17.8799             nan     0.1000    2.4049
##      9       15.7463             nan     0.1000    1.9826
##     10       14.2068             nan     0.1000    1.5676
##     20        6.2886             nan     0.1000    0.3123
##     40        3.9425             nan     0.1000   -0.0292
##     60        3.4702             nan     0.1000   -0.0300
##     80        3.1008             nan     0.1000   -0.0131
##    100        2.8624             nan     0.1000   -0.0200
##    120        2.6313             nan     0.1000   -0.0598
##    140        2.4602             nan     0.1000   -0.0303
##    160        2.3188             nan     0.1000   -0.0175
##    180        2.1772             nan     0.1000   -0.0405
##    200        2.0384             nan     0.1000   -0.0226
##    220        1.9101             nan     0.1000   -0.0192
##    240        1.7834             nan     0.1000   -0.0352
##    260        1.6996             nan     0.1000   -0.0160
##    280        1.6113             nan     0.1000   -0.0279
##    300        1.5246             nan     0.1000   -0.0093
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.5950             nan     0.1000    8.3794
##      2       43.8308             nan     0.1000    7.1214
##      3       37.8076             nan     0.1000    5.6016
##      4       32.8698             nan     0.1000    5.3205
##      5       27.8841             nan     0.1000    4.6785
##      6       24.5715             nan     0.1000    3.3179
##      7       21.5397             nan     0.1000    2.8568
##      8       18.8432             nan     0.1000    2.4496
##      9       16.8221             nan     0.1000    2.0181
##     10       15.2617             nan     0.1000    1.7046
##     20        6.6347             nan     0.1000    0.2833
##     40        4.2486             nan     0.1000   -0.0149
##     60        3.7787             nan     0.1000   -0.0190
##     80        3.4694             nan     0.1000   -0.0196
##    100        3.1947             nan     0.1000   -0.0373
##    120        3.0221             nan     0.1000   -0.0334
##    140        2.8385             nan     0.1000   -0.0276
##    160        2.6808             nan     0.1000   -0.0184
##    180        2.5116             nan     0.1000   -0.0285
##    200        2.4002             nan     0.1000   -0.0232
##    220        2.2783             nan     0.1000   -0.0380
##    240        2.1822             nan     0.1000   -0.0200
##    260        2.0864             nan     0.1000   -0.0368
##    280        1.9896             nan     0.1000   -0.0156
##    300        1.8993             nan     0.1000   -0.0295
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.6054             nan     0.1000   10.1424
##      2       42.5885             nan     0.1000    6.7176
##      3       35.6261             nan     0.1000    6.5610
##      4       30.0676             nan     0.1000    4.9483
##      5       25.4522             nan     0.1000    4.2897
##      6       21.8942             nan     0.1000    3.6321
##      7       18.8629             nan     0.1000    2.6835
##      8       16.4111             nan     0.1000    2.4666
##      9       14.4325             nan     0.1000    2.1045
##     10       12.4454             nan     0.1000    1.8635
##     20        5.2680             nan     0.1000    0.1775
##     40        3.0517             nan     0.1000   -0.0332
##     60        2.3445             nan     0.1000   -0.0274
##     80        1.9347             nan     0.1000   -0.0353
##    100        1.6053             nan     0.1000   -0.0128
##    120        1.3895             nan     0.1000   -0.0223
##    140        1.2440             nan     0.1000   -0.0083
##    160        1.0853             nan     0.1000   -0.0087
##    180        0.9643             nan     0.1000   -0.0029
##    200        0.8466             nan     0.1000   -0.0149
##    220        0.7593             nan     0.1000   -0.0125
##    240        0.6760             nan     0.1000   -0.0065
##    260        0.6214             nan     0.1000   -0.0128
##    280        0.5510             nan     0.1000   -0.0057
##    300        0.4948             nan     0.1000   -0.0075
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       49.9634             nan     0.1000    9.4014
##      2       41.8847             nan     0.1000    6.7158
##      3       35.2613             nan     0.1000    6.8687
##      4       29.7116             nan     0.1000    5.1108
##      5       25.2942             nan     0.1000    4.0963
##      6       21.4687             nan     0.1000    3.5762
##      7       18.4948             nan     0.1000    2.6667
##      8       15.9559             nan     0.1000    2.1789
##      9       13.9846             nan     0.1000    1.7370
##     10       12.4357             nan     0.1000    1.6308
##     20        5.1818             nan     0.1000    0.1900
##     40        3.2671             nan     0.1000   -0.0104
##     60        2.7578             nan     0.1000   -0.0256
##     80        2.4273             nan     0.1000   -0.0440
##    100        2.1215             nan     0.1000   -0.0201
##    120        1.8934             nan     0.1000   -0.0193
##    140        1.6978             nan     0.1000   -0.0406
##    160        1.5561             nan     0.1000   -0.0241
##    180        1.4241             nan     0.1000   -0.0314
##    200        1.2936             nan     0.1000   -0.0115
##    220        1.1826             nan     0.1000   -0.0127
##    240        1.0977             nan     0.1000   -0.0059
##    260        1.0046             nan     0.1000   -0.0160
##    280        0.9342             nan     0.1000   -0.0112
##    300        0.8744             nan     0.1000   -0.0290
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.6462             nan     0.1000    9.4627
##      2       42.5121             nan     0.1000    7.6239
##      3       35.8743             nan     0.1000    6.7384
##      4       30.4848             nan     0.1000    4.9400
##      5       26.1078             nan     0.1000    4.3720
##      6       22.3408             nan     0.1000    3.3748
##      7       19.2267             nan     0.1000    2.7493
##      8       16.7132             nan     0.1000    2.5066
##      9       14.7997             nan     0.1000    1.7164
##     10       13.1754             nan     0.1000    1.4000
##     20        5.9738             nan     0.1000    0.2272
##     40        3.9478             nan     0.1000   -0.0150
##     60        3.4497             nan     0.1000   -0.0536
##     80        3.1351             nan     0.1000   -0.0345
##    100        2.8915             nan     0.1000   -0.0718
##    120        2.6538             nan     0.1000   -0.0334
##    140        2.4287             nan     0.1000   -0.0142
##    160        2.2201             nan     0.1000   -0.0211
##    180        2.0823             nan     0.1000   -0.0655
##    200        1.9182             nan     0.1000   -0.0218
##    220        1.7647             nan     0.1000   -0.0211
##    240        1.6355             nan     0.1000   -0.0192
##    260        1.5266             nan     0.1000   -0.0150
##    280        1.4334             nan     0.1000   -0.0190
##    300        1.3407             nan     0.1000   -0.0257
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.3666             nan     0.0100    0.7745
##      2       61.6090             nan     0.0100    0.7517
##      3       60.8320             nan     0.0100    0.7918
##      4       60.0690             nan     0.0100    0.7150
##      5       59.3565             nan     0.0100    0.7037
##      6       58.6184             nan     0.0100    0.6815
##      7       57.8756             nan     0.0100    0.7089
##      8       57.2192             nan     0.0100    0.6890
##      9       56.5969             nan     0.0100    0.6257
##     10       55.9429             nan     0.0100    0.6358
##     20       49.7310             nan     0.0100    0.5790
##     40       40.1863             nan     0.0100    0.4189
##     60       32.9604             nan     0.0100    0.2811
##     80       27.5049             nan     0.0100    0.2147
##    100       23.3075             nan     0.0100    0.1674
##    120       20.0439             nan     0.0100    0.1249
##    140       17.4202             nan     0.0100    0.0916
##    160       15.3069             nan     0.0100    0.0914
##    180       13.5655             nan     0.0100    0.0534
##    200       12.1620             nan     0.0100    0.0569
##    220       10.9455             nan     0.0100    0.0415
##    240        9.9352             nan     0.0100    0.0391
##    260        9.0962             nan     0.0100    0.0338
##    280        8.3855             nan     0.0100    0.0300
##    300        7.7680             nan     0.0100    0.0221
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.3263             nan     0.0100    0.7230
##      2       61.6125             nan     0.0100    0.7691
##      3       60.8592             nan     0.0100    0.7790
##      4       60.0890             nan     0.0100    0.7706
##      5       59.3343             nan     0.0100    0.6986
##      6       58.6245             nan     0.0100    0.7201
##      7       57.8838             nan     0.0100    0.7145
##      8       57.1900             nan     0.0100    0.7245
##      9       56.4230             nan     0.0100    0.7275
##     10       55.7747             nan     0.0100    0.6913
##     20       49.6832             nan     0.0100    0.5411
##     40       40.0053             nan     0.0100    0.3880
##     60       32.9294             nan     0.0100    0.2949
##     80       27.5121             nan     0.0100    0.2086
##    100       23.2315             nan     0.0100    0.1756
##    120       19.8692             nan     0.0100    0.1261
##    140       17.2753             nan     0.0100    0.1054
##    160       15.1819             nan     0.0100    0.0764
##    180       13.4942             nan     0.0100    0.0621
##    200       12.1124             nan     0.0100    0.0519
##    220       10.9416             nan     0.0100    0.0515
##    240        9.9380             nan     0.0100    0.0171
##    260        9.0848             nan     0.0100    0.0393
##    280        8.3595             nan     0.0100    0.0307
##    300        7.7409             nan     0.0100    0.0142
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.4301             nan     0.0100    0.7655
##      2       61.6800             nan     0.0100    0.7735
##      3       60.8625             nan     0.0100    0.7237
##      4       60.0639             nan     0.0100    0.7596
##      5       59.3196             nan     0.0100    0.7467
##      6       58.6305             nan     0.0100    0.6892
##      7       57.8902             nan     0.0100    0.7108
##      8       57.2107             nan     0.0100    0.6974
##      9       56.4986             nan     0.0100    0.6530
##     10       55.8773             nan     0.0100    0.6445
##     20       49.8659             nan     0.0100    0.5837
##     40       40.3298             nan     0.0100    0.4291
##     60       33.0077             nan     0.0100    0.2939
##     80       27.5587             nan     0.0100    0.2352
##    100       23.2846             nan     0.0100    0.1626
##    120       19.9623             nan     0.0100    0.1516
##    140       17.3520             nan     0.0100    0.0961
##    160       15.2743             nan     0.0100    0.0711
##    180       13.6101             nan     0.0100    0.0628
##    200       12.1664             nan     0.0100    0.0507
##    220       11.0031             nan     0.0100    0.0404
##    240       10.0059             nan     0.0100    0.0360
##    260        9.1981             nan     0.0100    0.0285
##    280        8.5058             nan     0.0100    0.0207
##    300        7.9298             nan     0.0100    0.0208
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.1448             nan     0.0100    1.0570
##      2       61.1426             nan     0.0100    0.9295
##      3       60.1010             nan     0.0100    0.9063
##      4       59.1199             nan     0.0100    0.8452
##      5       58.1789             nan     0.0100    1.0006
##      6       57.2737             nan     0.0100    0.9247
##      7       56.3663             nan     0.0100    0.9373
##      8       55.4725             nan     0.0100    0.8479
##      9       54.5851             nan     0.0100    0.8785
##     10       53.7240             nan     0.0100    0.8542
##     20       45.8532             nan     0.0100    0.7787
##     40       33.7258             nan     0.0100    0.5471
##     60       25.3079             nan     0.0100    0.3943
##     80       19.3238             nan     0.0100    0.2046
##    100       15.1052             nan     0.0100    0.1924
##    120       12.0922             nan     0.0100    0.1081
##    140        9.8888             nan     0.0100    0.0924
##    160        8.3051             nan     0.0100    0.0500
##    180        7.1167             nan     0.0100    0.0381
##    200        6.2350             nan     0.0100    0.0351
##    220        5.5166             nan     0.0100    0.0268
##    240        4.9871             nan     0.0100    0.0219
##    260        4.5872             nan     0.0100    0.0122
##    280        4.2805             nan     0.0100    0.0053
##    300        4.0400             nan     0.0100    0.0096
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.0945             nan     0.0100    0.9786
##      2       61.1058             nan     0.0100    0.9705
##      3       60.0819             nan     0.0100    0.9370
##      4       59.1443             nan     0.0100    0.9501
##      5       58.1405             nan     0.0100    1.0091
##      6       57.2354             nan     0.0100    0.8829
##      7       56.3284             nan     0.0100    0.9700
##      8       55.4571             nan     0.0100    0.8453
##      9       54.5712             nan     0.0100    0.9180
##     10       53.7421             nan     0.0100    0.8607
##     20       45.8756             nan     0.0100    0.7534
##     40       33.6686             nan     0.0100    0.5340
##     60       25.2229             nan     0.0100    0.3554
##     80       19.3627             nan     0.0100    0.2164
##    100       15.1921             nan     0.0100    0.1725
##    120       12.2147             nan     0.0100    0.1181
##    140        9.9922             nan     0.0100    0.0832
##    160        8.4040             nan     0.0100    0.0616
##    180        7.2069             nan     0.0100    0.0563
##    200        6.3044             nan     0.0100    0.0246
##    220        5.6315             nan     0.0100    0.0232
##    240        5.0794             nan     0.0100    0.0218
##    260        4.6806             nan     0.0100    0.0092
##    280        4.3936             nan     0.0100    0.0122
##    300        4.1563             nan     0.0100    0.0073
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.1150             nan     0.0100    0.9482
##      2       61.0986             nan     0.0100    1.0356
##      3       60.0872             nan     0.0100    0.9700
##      4       59.0997             nan     0.0100    0.9243
##      5       58.0800             nan     0.0100    0.8601
##      6       57.1403             nan     0.0100    0.9063
##      7       56.2267             nan     0.0100    0.9634
##      8       55.2867             nan     0.0100    0.8690
##      9       54.3811             nan     0.0100    0.8761
##     10       53.5286             nan     0.0100    0.8338
##     20       45.7749             nan     0.0100    0.7665
##     40       33.5810             nan     0.0100    0.5015
##     60       25.3404             nan     0.0100    0.3192
##     80       19.4822             nan     0.0100    0.2446
##    100       15.3706             nan     0.0100    0.1522
##    120       12.3885             nan     0.0100    0.1043
##    140       10.1963             nan     0.0100    0.0853
##    160        8.5583             nan     0.0100    0.0634
##    180        7.3577             nan     0.0100    0.0370
##    200        6.4635             nan     0.0100    0.0328
##    220        5.8216             nan     0.0100    0.0234
##    240        5.2938             nan     0.0100    0.0191
##    260        4.9117             nan     0.0100    0.0092
##    280        4.6263             nan     0.0100    0.0061
##    300        4.4120             nan     0.0100    0.0036
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.0231             nan     0.0100    1.1812
##      2       60.9618             nan     0.0100    1.1027
##      3       59.9310             nan     0.0100    0.9604
##      4       58.8539             nan     0.0100    1.0465
##      5       57.7915             nan     0.0100    0.9646
##      6       56.7643             nan     0.0100    1.0248
##      7       55.7950             nan     0.0100    0.9430
##      8       54.7986             nan     0.0100    0.8944
##      9       53.8359             nan     0.0100    0.8376
##     10       52.8706             nan     0.0100    1.0157
##     20       44.5943             nan     0.0100    0.7622
##     40       31.9935             nan     0.0100    0.5424
##     60       23.3223             nan     0.0100    0.3732
##     80       17.3687             nan     0.0100    0.2161
##    100       13.3079             nan     0.0100    0.1714
##    120       10.4287             nan     0.0100    0.1139
##    140        8.3224             nan     0.0100    0.0582
##    160        6.8508             nan     0.0100    0.0498
##    180        5.7816             nan     0.0100    0.0445
##    200        5.0393             nan     0.0100    0.0229
##    220        4.4853             nan     0.0100    0.0100
##    240        4.0663             nan     0.0100    0.0108
##    260        3.7599             nan     0.0100   -0.0015
##    280        3.5120             nan     0.0100    0.0030
##    300        3.3146             nan     0.0100    0.0028
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.0327             nan     0.0100    1.0138
##      2       60.9327             nan     0.0100    1.0485
##      3       59.8853             nan     0.0100    1.0677
##      4       58.8338             nan     0.0100    0.8701
##      5       57.8401             nan     0.0100    1.0097
##      6       56.8605             nan     0.0100    0.8884
##      7       55.8420             nan     0.0100    0.9328
##      8       54.8976             nan     0.0100    0.9608
##      9       53.9937             nan     0.0100    0.9336
##     10       53.1256             nan     0.0100    0.9155
##     20       44.7059             nan     0.0100    0.6464
##     40       32.1114             nan     0.0100    0.4689
##     60       23.5668             nan     0.0100    0.3216
##     80       17.6377             nan     0.0100    0.2522
##    100       13.4722             nan     0.0100    0.1564
##    120       10.5870             nan     0.0100    0.1114
##    140        8.5470             nan     0.0100    0.0702
##    160        7.0802             nan     0.0100    0.0473
##    180        6.0294             nan     0.0100    0.0389
##    200        5.2730             nan     0.0100    0.0299
##    220        4.7071             nan     0.0100    0.0189
##    240        4.3004             nan     0.0100    0.0146
##    260        3.9658             nan     0.0100    0.0064
##    280        3.7325             nan     0.0100    0.0039
##    300        3.5570             nan     0.0100    0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.0248             nan     0.0100    1.1522
##      2       60.9473             nan     0.0100    1.1067
##      3       59.8982             nan     0.0100    1.1449
##      4       58.9010             nan     0.0100    1.0017
##      5       57.9225             nan     0.0100    0.9997
##      6       56.9768             nan     0.0100    0.9790
##      7       56.0655             nan     0.0100    0.9277
##      8       55.1037             nan     0.0100    0.9313
##      9       54.1773             nan     0.0100    0.9572
##     10       53.2365             nan     0.0100    0.8576
##     20       45.0227             nan     0.0100    0.6748
##     40       32.5234             nan     0.0100    0.4717
##     60       23.8651             nan     0.0100    0.3730
##     80       17.8533             nan     0.0100    0.1923
##    100       13.6444             nan     0.0100    0.1641
##    120       10.8320             nan     0.0100    0.0984
##    140        8.8153             nan     0.0100    0.0758
##    160        7.3935             nan     0.0100    0.0545
##    180        6.3434             nan     0.0100    0.0200
##    200        5.6228             nan     0.0100    0.0286
##    220        5.0891             nan     0.0100    0.0216
##    240        4.6669             nan     0.0100    0.0089
##    260        4.3635             nan     0.0100    0.0061
##    280        4.1354             nan     0.0100    0.0062
##    300        3.9642             nan     0.0100    0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.5047             nan     0.0500    3.7677
##      2       56.0395             nan     0.0500    3.5786
##      3       52.7698             nan     0.0500    3.1785
##      4       49.6318             nan     0.0500    2.8342
##      5       46.7270             nan     0.0500    2.5422
##      6       44.2266             nan     0.0500    1.9435
##      7       41.7435             nan     0.0500    1.9892
##      8       39.8471             nan     0.0500    1.7402
##      9       37.7459             nan     0.0500    1.9216
##     10       36.0801             nan     0.0500    1.7574
##     20       23.1064             nan     0.0500    0.9461
##     40       11.8094             nan     0.0500    0.2641
##     60        7.5838             nan     0.0500    0.0784
##     80        5.7318             nan     0.0500    0.0635
##    100        4.8478             nan     0.0500    0.0088
##    120        4.3847             nan     0.0500    0.0160
##    140        4.1562             nan     0.0500   -0.0013
##    160        4.0257             nan     0.0500    0.0037
##    180        3.9240             nan     0.0500   -0.0007
##    200        3.8486             nan     0.0500   -0.0040
##    220        3.7745             nan     0.0500   -0.0050
##    240        3.7282             nan     0.0500   -0.0155
##    260        3.6757             nan     0.0500   -0.0077
##    280        3.6302             nan     0.0500   -0.0013
##    300        3.5931             nan     0.0500   -0.0148
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.2775             nan     0.0500    3.8648
##      2       55.4966             nan     0.0500    3.3671
##      3       52.3059             nan     0.0500    3.1785
##      4       49.3441             nan     0.0500    2.9452
##      5       46.4254             nan     0.0500    2.5125
##      6       43.9975             nan     0.0500    2.1511
##      7       41.8546             nan     0.0500    2.2623
##      8       39.4682             nan     0.0500    2.0702
##      9       37.4819             nan     0.0500    1.9601
##     10       35.6142             nan     0.0500    1.7867
##     20       23.1449             nan     0.0500    0.9512
##     40       12.1609             nan     0.0500    0.2582
##     60        7.7496             nan     0.0500    0.0660
##     80        5.8031             nan     0.0500    0.0318
##    100        4.8564             nan     0.0500    0.0172
##    120        4.3577             nan     0.0500    0.0121
##    140        4.1329             nan     0.0500   -0.0125
##    160        4.0210             nan     0.0500   -0.0167
##    180        3.8969             nan     0.0500   -0.0128
##    200        3.8363             nan     0.0500   -0.0048
##    220        3.7871             nan     0.0500   -0.0160
##    240        3.7364             nan     0.0500   -0.0233
##    260        3.6812             nan     0.0500   -0.0068
##    280        3.6470             nan     0.0500   -0.0031
##    300        3.6168             nan     0.0500   -0.0170
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.4076             nan     0.0500    3.6972
##      2       55.6875             nan     0.0500    3.6430
##      3       52.4039             nan     0.0500    3.2392
##      4       49.5561             nan     0.0500    2.7492
##      5       46.6213             nan     0.0500    2.6466
##      6       43.9525             nan     0.0500    2.3052
##      7       41.5011             nan     0.0500    2.4400
##      8       39.5083             nan     0.0500    1.7253
##      9       37.6321             nan     0.0500    1.8658
##     10       35.8106             nan     0.0500    1.8050
##     20       22.9937             nan     0.0500    0.8894
##     40       11.9232             nan     0.0500    0.3240
##     60        7.6978             nan     0.0500    0.0949
##     80        5.8189             nan     0.0500    0.0542
##    100        4.9626             nan     0.0500    0.0175
##    120        4.5492             nan     0.0500   -0.0110
##    140        4.3535             nan     0.0500   -0.0063
##    160        4.2300             nan     0.0500   -0.0040
##    180        4.1492             nan     0.0500   -0.0149
##    200        4.0676             nan     0.0500   -0.0089
##    220        4.0177             nan     0.0500   -0.0094
##    240        3.9537             nan     0.0500   -0.0101
##    260        3.9014             nan     0.0500   -0.0129
##    280        3.8561             nan     0.0500   -0.0169
##    300        3.7889             nan     0.0500   -0.0061
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.9732             nan     0.0500    4.8411
##      2       53.3895             nan     0.0500    4.8857
##      3       49.0053             nan     0.0500    4.0397
##      4       45.2499             nan     0.0500    3.7254
##      5       41.6551             nan     0.0500    3.2348
##      6       38.5672             nan     0.0500    3.4127
##      7       35.8895             nan     0.0500    2.4979
##      8       33.6354             nan     0.0500    2.2063
##      9       31.2132             nan     0.0500    2.3220
##     10       29.0091             nan     0.0500    2.2349
##     20       15.1794             nan     0.0500    0.7898
##     40        6.2874             nan     0.0500    0.2063
##     60        4.0876             nan     0.0500    0.0270
##     80        3.4852             nan     0.0500   -0.0080
##    100        3.1574             nan     0.0500   -0.0400
##    120        2.9230             nan     0.0500   -0.0141
##    140        2.7211             nan     0.0500   -0.0176
##    160        2.5266             nan     0.0500   -0.0112
##    180        2.3917             nan     0.0500   -0.0220
##    200        2.2729             nan     0.0500   -0.0162
##    220        2.1668             nan     0.0500   -0.0175
##    240        2.0715             nan     0.0500   -0.0124
##    260        1.9873             nan     0.0500   -0.0093
##    280        1.9056             nan     0.0500   -0.0104
##    300        1.8280             nan     0.0500   -0.0146
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.0690             nan     0.0500    4.7817
##      2       53.4089             nan     0.0500    4.6694
##      3       49.2056             nan     0.0500    4.1396
##      4       45.3607             nan     0.0500    3.6699
##      5       41.7697             nan     0.0500    3.5068
##      6       38.4963             nan     0.0500    2.5788
##      7       35.5744             nan     0.0500    3.0243
##      8       32.8960             nan     0.0500    2.5875
##      9       30.5334             nan     0.0500    2.3667
##     10       28.3511             nan     0.0500    1.9365
##     20       14.7342             nan     0.0500    0.7733
##     40        6.0869             nan     0.0500    0.1687
##     60        4.0585             nan     0.0500    0.0310
##     80        3.5689             nan     0.0500   -0.0117
##    100        3.2529             nan     0.0500   -0.0070
##    120        3.0690             nan     0.0500   -0.0036
##    140        2.9074             nan     0.0500   -0.0097
##    160        2.7547             nan     0.0500   -0.0212
##    180        2.6300             nan     0.0500   -0.0102
##    200        2.5325             nan     0.0500   -0.0127
##    220        2.4488             nan     0.0500   -0.0101
##    240        2.3538             nan     0.0500   -0.0079
##    260        2.2704             nan     0.0500   -0.0106
##    280        2.1898             nan     0.0500   -0.0093
##    300        2.1107             nan     0.0500   -0.0033
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.3457             nan     0.0500    4.5497
##      2       53.7352             nan     0.0500    4.4274
##      3       49.4504             nan     0.0500    4.3940
##      4       45.7361             nan     0.0500    3.8289
##      5       42.3187             nan     0.0500    3.3659
##      6       39.2154             nan     0.0500    2.9067
##      7       36.4085             nan     0.0500    2.8827
##      8       33.6382             nan     0.0500    2.6308
##      9       31.2280             nan     0.0500    2.3426
##     10       29.0276             nan     0.0500    1.8932
##     20       15.2903             nan     0.0500    0.6671
##     40        6.4833             nan     0.0500    0.1260
##     60        4.4829             nan     0.0500   -0.0042
##     80        3.9029             nan     0.0500   -0.0028
##    100        3.6449             nan     0.0500   -0.0123
##    120        3.4567             nan     0.0500   -0.0344
##    140        3.2980             nan     0.0500   -0.0188
##    160        3.1484             nan     0.0500   -0.0196
##    180        3.0318             nan     0.0500   -0.0157
##    200        2.9109             nan     0.0500   -0.0102
##    220        2.8270             nan     0.0500   -0.0177
##    240        2.7271             nan     0.0500   -0.0157
##    260        2.6546             nan     0.0500   -0.0067
##    280        2.5600             nan     0.0500   -0.0280
##    300        2.4856             nan     0.0500   -0.0151
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.6555             nan     0.0500    5.4898
##      2       52.7172             nan     0.0500    4.8280
##      3       48.3536             nan     0.0500    4.1629
##      4       44.3129             nan     0.0500    3.7626
##      5       40.7107             nan     0.0500    3.4519
##      6       37.3951             nan     0.0500    3.6380
##      7       34.2625             nan     0.0500    2.9334
##      8       31.7380             nan     0.0500    2.5877
##      9       29.3210             nan     0.0500    2.3554
##     10       27.1057             nan     0.0500    2.0537
##     20       12.8948             nan     0.0500    0.8838
##     40        4.9318             nan     0.0500    0.1199
##     60        3.3565             nan     0.0500    0.0068
##     80        2.7717             nan     0.0500   -0.0035
##    100        2.4331             nan     0.0500   -0.0127
##    120        2.1977             nan     0.0500   -0.0158
##    140        1.9806             nan     0.0500   -0.0136
##    160        1.8217             nan     0.0500   -0.0191
##    180        1.6777             nan     0.0500   -0.0134
##    200        1.5573             nan     0.0500   -0.0139
##    220        1.4476             nan     0.0500   -0.0154
##    240        1.3314             nan     0.0500   -0.0062
##    260        1.2583             nan     0.0500   -0.0125
##    280        1.1744             nan     0.0500   -0.0130
##    300        1.0928             nan     0.0500   -0.0097
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.7410             nan     0.0500    5.4032
##      2       52.8961             nan     0.0500    4.9490
##      3       48.3202             nan     0.0500    3.9978
##      4       44.2521             nan     0.0500    4.1680
##      5       40.6345             nan     0.0500    3.5487
##      6       37.3365             nan     0.0500    3.1174
##      7       34.3868             nan     0.0500    3.0625
##      8       31.6272             nan     0.0500    2.9383
##      9       29.3122             nan     0.0500    2.4324
##     10       27.0507             nan     0.0500    2.2717
##     20       13.0452             nan     0.0500    0.8046
##     40        5.1410             nan     0.0500    0.1283
##     60        3.6367             nan     0.0500   -0.0036
##     80        3.1242             nan     0.0500    0.0044
##    100        2.8473             nan     0.0500   -0.0204
##    120        2.6180             nan     0.0500   -0.0218
##    140        2.4372             nan     0.0500   -0.0248
##    160        2.2681             nan     0.0500   -0.0075
##    180        2.1267             nan     0.0500   -0.0161
##    200        1.9981             nan     0.0500   -0.0139
##    220        1.8901             nan     0.0500   -0.0152
##    240        1.7892             nan     0.0500   -0.0174
##    260        1.6901             nan     0.0500   -0.0125
##    280        1.6133             nan     0.0500   -0.0183
##    300        1.5347             nan     0.0500   -0.0112
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.7746             nan     0.0500    5.3603
##      2       52.8792             nan     0.0500    4.6078
##      3       48.6105             nan     0.0500    4.2651
##      4       44.5674             nan     0.0500    4.0778
##      5       40.8576             nan     0.0500    3.8866
##      6       37.6271             nan     0.0500    3.2488
##      7       34.4510             nan     0.0500    2.6504
##      8       31.8512             nan     0.0500    2.5021
##      9       29.3322             nan     0.0500    2.2103
##     10       27.3026             nan     0.0500    2.0951
##     20       13.3943             nan     0.0500    0.7328
##     40        5.3889             nan     0.0500    0.0966
##     60        3.9447             nan     0.0500    0.0174
##     80        3.4681             nan     0.0500   -0.0155
##    100        3.1949             nan     0.0500   -0.0243
##    120        2.9657             nan     0.0500   -0.0172
##    140        2.7987             nan     0.0500   -0.0123
##    160        2.6507             nan     0.0500   -0.0213
##    180        2.5054             nan     0.0500   -0.0159
##    200        2.4032             nan     0.0500   -0.0183
##    220        2.2974             nan     0.0500   -0.0198
##    240        2.2013             nan     0.0500   -0.0145
##    260        2.1075             nan     0.0500   -0.0102
##    280        2.0293             nan     0.0500   -0.0103
##    300        1.9488             nan     0.0500   -0.0078
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.0803             nan     0.1000    7.5313
##      2       49.8452             nan     0.1000    6.4820
##      3       44.4064             nan     0.1000    5.3005
##      4       39.5259             nan     0.1000    4.3813
##      5       35.7528             nan     0.1000    3.4928
##      6       32.4886             nan     0.1000    3.1382
##      7       29.2798             nan     0.1000    2.6986
##      8       27.0270             nan     0.1000    2.3115
##      9       24.7436             nan     0.1000    1.6704
##     10       22.8004             nan     0.1000    1.7838
##     20       11.8109             nan     0.1000    0.3964
##     40        5.7387             nan     0.1000    0.0661
##     60        4.4420             nan     0.1000    0.0035
##     80        4.0839             nan     0.1000   -0.0017
##    100        3.9172             nan     0.1000   -0.0297
##    120        3.8046             nan     0.1000   -0.0303
##    140        3.7075             nan     0.1000   -0.0330
##    160        3.6285             nan     0.1000   -0.0092
##    180        3.5439             nan     0.1000   -0.0183
##    200        3.4701             nan     0.1000   -0.0327
##    220        3.3994             nan     0.1000   -0.0170
##    240        3.3283             nan     0.1000   -0.0206
##    260        3.2703             nan     0.1000   -0.0390
##    280        3.2279             nan     0.1000   -0.0189
##    300        3.1826             nan     0.1000   -0.0113
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.2672             nan     0.1000    7.1844
##      2       48.6712             nan     0.1000    6.1719
##      3       43.1145             nan     0.1000    4.9617
##      4       38.7978             nan     0.1000    4.1956
##      5       34.6457             nan     0.1000    3.5746
##      6       31.7696             nan     0.1000    2.8539
##      7       28.6772             nan     0.1000    2.7944
##      8       26.1788             nan     0.1000    2.3845
##      9       24.4291             nan     0.1000    1.6301
##     10       22.5347             nan     0.1000    1.6783
##     20       11.6077             nan     0.1000    0.6700
##     40        5.6273             nan     0.1000    0.0853
##     60        4.3418             nan     0.1000    0.0061
##     80        4.0344             nan     0.1000    0.0034
##    100        3.8896             nan     0.1000   -0.0084
##    120        3.8039             nan     0.1000   -0.0228
##    140        3.7190             nan     0.1000   -0.0123
##    160        3.6533             nan     0.1000   -0.0226
##    180        3.5778             nan     0.1000   -0.0046
##    200        3.4911             nan     0.1000   -0.0160
##    220        3.4363             nan     0.1000   -0.0322
##    240        3.3747             nan     0.1000   -0.0105
##    260        3.3236             nan     0.1000   -0.0145
##    280        3.2648             nan     0.1000   -0.0037
##    300        3.2304             nan     0.1000   -0.0094
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.6990             nan     0.1000    7.2768
##      2       49.6177             nan     0.1000    6.0675
##      3       44.4808             nan     0.1000    4.9048
##      4       39.9892             nan     0.1000    4.3990
##      5       35.7152             nan     0.1000    3.9322
##      6       31.9686             nan     0.1000    2.8026
##      7       28.5617             nan     0.1000    2.7880
##      8       26.1725             nan     0.1000    2.2050
##      9       24.1058             nan     0.1000    2.1288
##     10       22.3283             nan     0.1000    1.6598
##     20       11.9049             nan     0.1000    0.6622
##     40        5.7876             nan     0.1000    0.0838
##     60        4.5613             nan     0.1000    0.0108
##     80        4.2351             nan     0.1000   -0.0362
##    100        4.0641             nan     0.1000   -0.0189
##    120        3.9528             nan     0.1000   -0.0228
##    140        3.8823             nan     0.1000   -0.0520
##    160        3.7857             nan     0.1000   -0.0046
##    180        3.7246             nan     0.1000   -0.0288
##    200        3.6581             nan     0.1000   -0.0231
##    220        3.5823             nan     0.1000   -0.0199
##    240        3.5387             nan     0.1000   -0.0081
##    260        3.4868             nan     0.1000   -0.0212
##    280        3.4314             nan     0.1000   -0.0171
##    300        3.3769             nan     0.1000   -0.0173
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.7290             nan     0.1000    9.9066
##      2       45.6315             nan     0.1000    8.6165
##      3       38.9216             nan     0.1000    5.6129
##      4       33.4303             nan     0.1000    4.8112
##      5       28.5054             nan     0.1000    4.7638
##      6       24.6004             nan     0.1000    3.7476
##      7       21.3019             nan     0.1000    3.2325
##      8       18.4851             nan     0.1000    2.4128
##      9       16.3974             nan     0.1000    1.7965
##     10       14.6864             nan     0.1000    1.7041
##     20        6.0140             nan     0.1000    0.2691
##     40        3.4859             nan     0.1000   -0.0160
##     60        2.8725             nan     0.1000   -0.0297
##     80        2.5612             nan     0.1000   -0.0280
##    100        2.3114             nan     0.1000   -0.0246
##    120        2.1012             nan     0.1000   -0.0331
##    140        1.9231             nan     0.1000   -0.0209
##    160        1.7715             nan     0.1000   -0.0442
##    180        1.6248             nan     0.1000   -0.0098
##    200        1.4951             nan     0.1000   -0.0213
##    220        1.4162             nan     0.1000   -0.0162
##    240        1.3494             nan     0.1000   -0.0130
##    260        1.2600             nan     0.1000   -0.0070
##    280        1.1910             nan     0.1000   -0.0118
##    300        1.1261             nan     0.1000   -0.0153
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.2377             nan     0.1000    9.4861
##      2       45.1783             nan     0.1000    7.9912
##      3       38.9060             nan     0.1000    6.6918
##      4       33.1962             nan     0.1000    5.1613
##      5       28.5414             nan     0.1000    4.1617
##      6       24.7137             nan     0.1000    3.8046
##      7       21.3667             nan     0.1000    3.3157
##      8       18.6283             nan     0.1000    2.3949
##      9       16.3098             nan     0.1000    2.0543
##     10       14.4778             nan     0.1000    1.6397
##     20        6.0105             nan     0.1000    0.3172
##     40        3.5240             nan     0.1000   -0.0144
##     60        3.0721             nan     0.1000   -0.0438
##     80        2.7545             nan     0.1000   -0.0212
##    100        2.5652             nan     0.1000   -0.0618
##    120        2.3642             nan     0.1000   -0.0091
##    140        2.2069             nan     0.1000   -0.0150
##    160        2.0859             nan     0.1000   -0.0312
##    180        1.9946             nan     0.1000   -0.0207
##    200        1.8617             nan     0.1000   -0.0149
##    220        1.7648             nan     0.1000   -0.0204
##    240        1.7029             nan     0.1000   -0.0175
##    260        1.6133             nan     0.1000   -0.0136
##    280        1.5417             nan     0.1000   -0.0215
##    300        1.4541             nan     0.1000   -0.0179
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.2146             nan     0.1000    9.3483
##      2       45.1602             nan     0.1000    8.6411
##      3       38.5428             nan     0.1000    7.0999
##      4       32.8184             nan     0.1000    5.8876
##      5       27.8858             nan     0.1000    4.2108
##      6       24.4795             nan     0.1000    3.6876
##      7       21.7731             nan     0.1000    2.9373
##      8       19.2584             nan     0.1000    2.5896
##      9       17.0369             nan     0.1000    2.2264
##     10       15.2640             nan     0.1000    1.6736
##     20        6.8441             nan     0.1000    0.4335
##     40        3.9777             nan     0.1000   -0.0053
##     60        3.4322             nan     0.1000   -0.0283
##     80        3.1130             nan     0.1000   -0.0325
##    100        2.9361             nan     0.1000   -0.0429
##    120        2.7311             nan     0.1000   -0.0213
##    140        2.5908             nan     0.1000   -0.0211
##    160        2.4328             nan     0.1000   -0.0197
##    180        2.3119             nan     0.1000   -0.0261
##    200        2.1803             nan     0.1000   -0.0191
##    220        2.0844             nan     0.1000   -0.0252
##    240        1.9988             nan     0.1000   -0.0223
##    260        1.9351             nan     0.1000   -0.0349
##    280        1.8526             nan     0.1000   -0.0164
##    300        1.7735             nan     0.1000   -0.0290
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.6829             nan     0.1000    9.4575
##      2       43.9227             nan     0.1000    8.7425
##      3       37.3367             nan     0.1000    6.6163
##      4       31.4983             nan     0.1000    6.0197
##      5       26.6240             nan     0.1000    4.4412
##      6       22.5364             nan     0.1000    3.9279
##      7       19.4797             nan     0.1000    2.8998
##      8       16.7500             nan     0.1000    2.4887
##      9       14.5386             nan     0.1000    2.1272
##     10       12.8489             nan     0.1000    1.5969
##     20        4.8985             nan     0.1000    0.2027
##     40        2.6990             nan     0.1000    0.0051
##     60        2.0987             nan     0.1000   -0.0021
##     80        1.7814             nan     0.1000   -0.0334
##    100        1.5208             nan     0.1000   -0.0424
##    120        1.3232             nan     0.1000   -0.0152
##    140        1.1465             nan     0.1000   -0.0172
##    160        1.0038             nan     0.1000   -0.0061
##    180        0.8904             nan     0.1000   -0.0120
##    200        0.8049             nan     0.1000   -0.0174
##    220        0.7198             nan     0.1000   -0.0115
##    240        0.6522             nan     0.1000   -0.0102
##    260        0.5868             nan     0.1000   -0.0060
##    280        0.5340             nan     0.1000   -0.0056
##    300        0.4855             nan     0.1000   -0.0068
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.4775             nan     0.1000   10.7340
##      2       43.9951             nan     0.1000    9.1355
##      3       37.0396             nan     0.1000    6.6694
##      4       31.3262             nan     0.1000    5.4702
##      5       26.7468             nan     0.1000    4.4205
##      6       22.8081             nan     0.1000    3.5088
##      7       19.6570             nan     0.1000    3.2550
##      8       17.0246             nan     0.1000    2.6802
##      9       14.9335             nan     0.1000    2.1329
##     10       13.0306             nan     0.1000    1.7657
##     20        5.0612             nan     0.1000    0.2234
##     40        3.0807             nan     0.1000   -0.0271
##     60        2.5598             nan     0.1000   -0.0504
##     80        2.2123             nan     0.1000   -0.0337
##    100        1.9912             nan     0.1000   -0.0299
##    120        1.7813             nan     0.1000   -0.0190
##    140        1.5883             nan     0.1000   -0.0245
##    160        1.4691             nan     0.1000   -0.0220
##    180        1.3515             nan     0.1000   -0.0289
##    200        1.2447             nan     0.1000   -0.0226
##    220        1.1206             nan     0.1000   -0.0152
##    240        1.0433             nan     0.1000   -0.0175
##    260        0.9514             nan     0.1000   -0.0061
##    280        0.8781             nan     0.1000   -0.0134
##    300        0.8163             nan     0.1000   -0.0180
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.5734             nan     0.1000   10.2438
##      2       43.7559             nan     0.1000    8.0896
##      3       36.3089             nan     0.1000    7.4155
##      4       30.8100             nan     0.1000    5.3545
##      5       26.1630             nan     0.1000    4.8181
##      6       22.3324             nan     0.1000    4.0317
##      7       19.0919             nan     0.1000    2.8747
##      8       16.7111             nan     0.1000    2.3354
##      9       14.4597             nan     0.1000    1.8226
##     10       12.7555             nan     0.1000    1.9091
##     20        5.3070             nan     0.1000    0.2227
##     40        3.4909             nan     0.1000   -0.0119
##     60        3.0583             nan     0.1000   -0.0497
##     80        2.7795             nan     0.1000   -0.0440
##    100        2.5366             nan     0.1000   -0.0360
##    120        2.2867             nan     0.1000   -0.0200
##    140        2.1206             nan     0.1000   -0.0243
##    160        1.9255             nan     0.1000   -0.0256
##    180        1.7683             nan     0.1000   -0.0373
##    200        1.6424             nan     0.1000   -0.0112
##    220        1.5166             nan     0.1000   -0.0214
##    240        1.4146             nan     0.1000   -0.0242
##    260        1.3280             nan     0.1000   -0.0139
##    280        1.2480             nan     0.1000   -0.0141
##    300        1.1826             nan     0.1000   -0.0210
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.2055             nan     0.0100    0.7649
##      2       58.4764             nan     0.0100    0.6512
##      3       57.7538             nan     0.0100    0.8148
##      4       57.0655             nan     0.0100    0.6700
##      5       56.4288             nan     0.0100    0.6855
##      6       55.7148             nan     0.0100    0.6332
##      7       55.0100             nan     0.0100    0.7080
##      8       54.3008             nan     0.0100    0.7489
##      9       53.6347             nan     0.0100    0.6366
##     10       53.0220             nan     0.0100    0.6009
##     20       47.3155             nan     0.0100    0.5153
##     40       38.1744             nan     0.0100    0.3640
##     60       31.3245             nan     0.0100    0.2694
##     80       26.0187             nan     0.0100    0.2143
##    100       21.9611             nan     0.0100    0.1632
##    120       18.7910             nan     0.0100    0.1068
##    140       16.3227             nan     0.0100    0.0925
##    160       14.3408             nan     0.0100    0.0745
##    180       12.7449             nan     0.0100    0.0695
##    200       11.4512             nan     0.0100    0.0451
##    220       10.4004             nan     0.0100    0.0416
##    240        9.4956             nan     0.0100    0.0405
##    260        8.7639             nan     0.0100    0.0177
##    280        8.1107             nan     0.0100    0.0228
##    300        7.5627             nan     0.0100    0.0236
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.2172             nan     0.0100    0.7959
##      2       58.5000             nan     0.0100    0.7490
##      3       57.7432             nan     0.0100    0.6952
##      4       57.0722             nan     0.0100    0.7074
##      5       56.4215             nan     0.0100    0.5754
##      6       55.7722             nan     0.0100    0.6623
##      7       55.1252             nan     0.0100    0.6396
##      8       54.4376             nan     0.0100    0.6706
##      9       53.8144             nan     0.0100    0.6113
##     10       53.1204             nan     0.0100    0.6285
##     20       47.4312             nan     0.0100    0.5403
##     40       38.1606             nan     0.0100    0.3735
##     60       31.3166             nan     0.0100    0.3128
##     80       26.0916             nan     0.0100    0.2193
##    100       21.9502             nan     0.0100    0.1467
##    120       18.8509             nan     0.0100    0.1279
##    140       16.3704             nan     0.0100    0.0999
##    160       14.3454             nan     0.0100    0.0862
##    180       12.7406             nan     0.0100    0.0481
##    200       11.4441             nan     0.0100    0.0528
##    220       10.3588             nan     0.0100    0.0419
##    240        9.4300             nan     0.0100    0.0376
##    260        8.6832             nan     0.0100    0.0310
##    280        8.0290             nan     0.0100    0.0174
##    300        7.5103             nan     0.0100    0.0190
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.2296             nan     0.0100    0.7425
##      2       58.5102             nan     0.0100    0.7955
##      3       57.8199             nan     0.0100    0.6897
##      4       57.1155             nan     0.0100    0.7518
##      5       56.4129             nan     0.0100    0.6998
##      6       55.6816             nan     0.0100    0.6536
##      7       54.9794             nan     0.0100    0.6278
##      8       54.3355             nan     0.0100    0.6689
##      9       53.6976             nan     0.0100    0.6181
##     10       53.0504             nan     0.0100    0.4874
##     20       47.2064             nan     0.0100    0.5202
##     40       38.1963             nan     0.0100    0.3396
##     60       31.4038             nan     0.0100    0.2507
##     80       26.1567             nan     0.0100    0.2194
##    100       22.0882             nan     0.0100    0.1706
##    120       18.8968             nan     0.0100    0.1227
##    140       16.3688             nan     0.0100    0.0879
##    160       14.4039             nan     0.0100    0.0648
##    180       12.7744             nan     0.0100    0.0663
##    200       11.4732             nan     0.0100    0.0379
##    220       10.3995             nan     0.0100    0.0443
##    240        9.5005             nan     0.0100    0.0298
##    260        8.7740             nan     0.0100    0.0295
##    280        8.1373             nan     0.0100    0.0192
##    300        7.5859             nan     0.0100    0.0206
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.9881             nan     0.0100    0.9252
##      2       57.9850             nan     0.0100    0.8941
##      3       57.0249             nan     0.0100    0.9680
##      4       56.0919             nan     0.0100    1.0039
##      5       55.1750             nan     0.0100    1.0136
##      6       54.2720             nan     0.0100    0.9128
##      7       53.3838             nan     0.0100    0.9110
##      8       52.5337             nan     0.0100    0.8567
##      9       51.7094             nan     0.0100    0.8253
##     10       50.9043             nan     0.0100    0.8165
##     20       43.5253             nan     0.0100    0.5899
##     40       31.9691             nan     0.0100    0.4550
##     60       24.0826             nan     0.0100    0.3469
##     80       18.5107             nan     0.0100    0.2061
##    100       14.5277             nan     0.0100    0.1415
##    120       11.7414             nan     0.0100    0.1090
##    140        9.7198             nan     0.0100    0.0588
##    160        8.2472             nan     0.0100    0.0605
##    180        7.1113             nan     0.0100    0.0409
##    200        6.2665             nan     0.0100    0.0235
##    220        5.6294             nan     0.0100    0.0133
##    240        5.1207             nan     0.0100    0.0140
##    260        4.7755             nan     0.0100    0.0110
##    280        4.4774             nan     0.0100    0.0047
##    300        4.2470             nan     0.0100    0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.0213             nan     0.0100    1.0300
##      2       58.0681             nan     0.0100    1.0267
##      3       57.1088             nan     0.0100    0.8565
##      4       56.2438             nan     0.0100    0.9469
##      5       55.3183             nan     0.0100    0.9092
##      6       54.4064             nan     0.0100    0.9397
##      7       53.5457             nan     0.0100    0.8244
##      8       52.6850             nan     0.0100    0.7748
##      9       51.8533             nan     0.0100    0.7886
##     10       51.0059             nan     0.0100    0.7634
##     20       43.5978             nan     0.0100    0.6902
##     40       32.1357             nan     0.0100    0.4803
##     60       24.1292             nan     0.0100    0.2968
##     80       18.5851             nan     0.0100    0.2235
##    100       14.5994             nan     0.0100    0.1744
##    120       11.7401             nan     0.0100    0.1218
##    140        9.7248             nan     0.0100    0.1024
##    160        8.2642             nan     0.0100    0.0579
##    180        7.1507             nan     0.0100    0.0379
##    200        6.3387             nan     0.0100    0.0274
##    220        5.7445             nan     0.0100    0.0180
##    240        5.2660             nan     0.0100    0.0187
##    260        4.8890             nan     0.0100    0.0057
##    280        4.6056             nan     0.0100    0.0013
##    300        4.3881             nan     0.0100    0.0047
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.0303             nan     0.0100    0.9225
##      2       58.0235             nan     0.0100    0.9958
##      3       57.0225             nan     0.0100    0.9168
##      4       56.1281             nan     0.0100    0.8586
##      5       55.2039             nan     0.0100    0.9568
##      6       54.2922             nan     0.0100    0.9264
##      7       53.3621             nan     0.0100    0.8998
##      8       52.4992             nan     0.0100    0.7910
##      9       51.6467             nan     0.0100    0.8210
##     10       50.8060             nan     0.0100    0.7185
##     20       43.3211             nan     0.0100    0.6848
##     40       32.0377             nan     0.0100    0.5019
##     60       24.1766             nan     0.0100    0.3266
##     80       18.6001             nan     0.0100    0.2502
##    100       14.7534             nan     0.0100    0.1393
##    120       11.9649             nan     0.0100    0.0966
##    140        9.9451             nan     0.0100    0.0732
##    160        8.4430             nan     0.0100    0.0672
##    180        7.3253             nan     0.0100    0.0401
##    200        6.5429             nan     0.0100    0.0263
##    220        5.9051             nan     0.0100    0.0210
##    240        5.4338             nan     0.0100    0.0170
##    260        5.0842             nan     0.0100    0.0008
##    280        4.8305             nan     0.0100    0.0014
##    300        4.6088             nan     0.0100    0.0043
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.9433             nan     0.0100    1.0394
##      2       57.9199             nan     0.0100    0.9604
##      3       56.9376             nan     0.0100    0.9595
##      4       55.9689             nan     0.0100    0.9725
##      5       55.0147             nan     0.0100    0.9986
##      6       54.0169             nan     0.0100    0.9901
##      7       53.1426             nan     0.0100    0.8499
##      8       52.2802             nan     0.0100    0.9094
##      9       51.4344             nan     0.0100    0.9726
##     10       50.5472             nan     0.0100    0.7647
##     20       42.6206             nan     0.0100    0.6987
##     40       30.7592             nan     0.0100    0.4881
##     60       22.5728             nan     0.0100    0.3365
##     80       16.9905             nan     0.0100    0.2175
##    100       13.0073             nan     0.0100    0.1553
##    120       10.2943             nan     0.0100    0.0872
##    140        8.3465             nan     0.0100    0.0584
##    160        6.9901             nan     0.0100    0.0422
##    180        5.9816             nan     0.0100    0.0316
##    200        5.2494             nan     0.0100    0.0267
##    220        4.7119             nan     0.0100    0.0136
##    240        4.2793             nan     0.0100    0.0059
##    260        3.9677             nan     0.0100    0.0028
##    280        3.7206             nan     0.0100   -0.0032
##    300        3.5405             nan     0.0100    0.0030
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.9771             nan     0.0100    1.0138
##      2       57.9388             nan     0.0100    1.0215
##      3       56.9057             nan     0.0100    0.8507
##      4       55.9144             nan     0.0100    0.8979
##      5       54.9286             nan     0.0100    0.8803
##      6       54.0013             nan     0.0100    0.9204
##      7       53.0771             nan     0.0100    0.9776
##      8       52.1396             nan     0.0100    0.8768
##      9       51.2288             nan     0.0100    0.8359
##     10       50.3323             nan     0.0100    0.7550
##     20       42.4686             nan     0.0100    0.5999
##     40       30.6127             nan     0.0100    0.4739
##     60       22.5577             nan     0.0100    0.3430
##     80       16.9177             nan     0.0100    0.2607
##    100       12.9853             nan     0.0100    0.1590
##    120       10.2636             nan     0.0100    0.0974
##    140        8.3866             nan     0.0100    0.0750
##    160        7.0250             nan     0.0100    0.0379
##    180        6.0291             nan     0.0100    0.0290
##    200        5.3336             nan     0.0100    0.0175
##    220        4.8431             nan     0.0100    0.0155
##    240        4.4717             nan     0.0100    0.0085
##    260        4.1905             nan     0.0100    0.0039
##    280        3.9739             nan     0.0100    0.0021
##    300        3.8022             nan     0.0100    0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.9336             nan     0.0100    0.9507
##      2       57.8893             nan     0.0100    0.9064
##      3       56.8940             nan     0.0100    1.0292
##      4       55.8967             nan     0.0100    0.8574
##      5       54.9416             nan     0.0100    1.0302
##      6       53.9858             nan     0.0100    1.0444
##      7       53.0169             nan     0.0100    0.7872
##      8       52.1217             nan     0.0100    0.8675
##      9       51.2298             nan     0.0100    0.8214
##     10       50.3466             nan     0.0100    0.9471
##     20       42.3914             nan     0.0100    0.7441
##     40       30.5107             nan     0.0100    0.4414
##     60       22.5680             nan     0.0100    0.3294
##     80       17.0209             nan     0.0100    0.1983
##    100       13.1402             nan     0.0100    0.1475
##    120       10.4200             nan     0.0100    0.1067
##    140        8.5383             nan     0.0100    0.0598
##    160        7.1786             nan     0.0100    0.0489
##    180        6.2518             nan     0.0100    0.0351
##    200        5.5555             nan     0.0100    0.0257
##    220        5.0721             nan     0.0100    0.0197
##    240        4.7175             nan     0.0100    0.0119
##    260        4.4548             nan     0.0100    0.0058
##    280        4.2469             nan     0.0100    0.0015
##    300        4.0950             nan     0.0100    0.0043
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.5437             nan     0.0500    3.7098
##      2       53.0697             nan     0.0500    3.1212
##      3       49.9928             nan     0.0500    2.9125
##      4       47.1722             nan     0.0500    2.7779
##      5       44.9008             nan     0.0500    2.1183
##      6       42.6793             nan     0.0500    2.4575
##      7       40.5084             nan     0.0500    2.1310
##      8       38.3232             nan     0.0500    2.0163
##      9       36.7718             nan     0.0500    1.7894
##     10       34.8427             nan     0.0500    1.7069
##     20       22.3214             nan     0.0500    0.9520
##     40       11.4992             nan     0.0500    0.2846
##     60        7.5983             nan     0.0500    0.1164
##     80        5.7645             nan     0.0500    0.0518
##    100        4.9674             nan     0.0500    0.0154
##    120        4.5245             nan     0.0500   -0.0094
##    140        4.3442             nan     0.0500   -0.0069
##    160        4.2481             nan     0.0500   -0.0163
##    180        4.1385             nan     0.0500   -0.0010
##    200        4.0504             nan     0.0500   -0.0070
##    220        3.9910             nan     0.0500   -0.0034
##    240        3.9255             nan     0.0500   -0.0132
##    260        3.8697             nan     0.0500    0.0007
##    280        3.8276             nan     0.0500   -0.0178
##    300        3.7703             nan     0.0500   -0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.2519             nan     0.0500    3.6212
##      2       52.5945             nan     0.0500    3.3292
##      3       49.3846             nan     0.0500    2.7744
##      4       46.6029             nan     0.0500    2.5891
##      5       44.1908             nan     0.0500    2.4527
##      6       41.8764             nan     0.0500    2.1739
##      7       39.7937             nan     0.0500    2.1882
##      8       37.9501             nan     0.0500    1.7507
##      9       35.9900             nan     0.0500    1.7515
##     10       34.2726             nan     0.0500    1.7283
##     20       21.3945             nan     0.0500    0.8414
##     40       11.4588             nan     0.0500    0.2039
##     60        7.6137             nan     0.0500    0.0855
##     80        5.8009             nan     0.0500    0.0390
##    100        4.9844             nan     0.0500    0.0261
##    120        4.5739             nan     0.0500    0.0015
##    140        4.4265             nan     0.0500   -0.0165
##    160        4.3170             nan     0.0500   -0.0151
##    180        4.2502             nan     0.0500   -0.0170
##    200        4.1859             nan     0.0500   -0.0081
##    220        4.1156             nan     0.0500   -0.0113
##    240        4.0385             nan     0.0500   -0.0115
##    260        3.9913             nan     0.0500   -0.0214
##    280        3.9414             nan     0.0500   -0.0148
##    300        3.8936             nan     0.0500   -0.0116
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.1189             nan     0.0500    3.8541
##      2       52.6946             nan     0.0500    3.3061
##      3       49.7295             nan     0.0500    2.9384
##      4       47.2452             nan     0.0500    2.2361
##      5       44.5687             nan     0.0500    2.7725
##      6       41.9633             nan     0.0500    2.3470
##      7       39.8315             nan     0.0500    1.8245
##      8       37.7016             nan     0.0500    1.7331
##      9       35.6770             nan     0.0500    1.7630
##     10       33.8346             nan     0.0500    1.6129
##     20       21.3800             nan     0.0500    0.7759
##     40       11.2302             nan     0.0500    0.2535
##     60        7.3073             nan     0.0500    0.0740
##     80        5.6751             nan     0.0500    0.0505
##    100        4.9578             nan     0.0500    0.0039
##    120        4.6405             nan     0.0500    0.0044
##    140        4.4612             nan     0.0500   -0.0060
##    160        4.3504             nan     0.0500   -0.0033
##    180        4.2651             nan     0.0500   -0.0026
##    200        4.1977             nan     0.0500   -0.0060
##    220        4.1340             nan     0.0500   -0.0083
##    240        4.0835             nan     0.0500   -0.0074
##    260        4.0380             nan     0.0500   -0.0078
##    280        3.9891             nan     0.0500   -0.0203
##    300        3.9472             nan     0.0500   -0.0091
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.0031             nan     0.0500    4.7536
##      2       50.6186             nan     0.0500    4.6796
##      3       46.7006             nan     0.0500    3.6753
##      4       43.2462             nan     0.0500    3.6595
##      5       40.0116             nan     0.0500    3.1013
##      6       36.9215             nan     0.0500    3.1171
##      7       34.2902             nan     0.0500    2.6227
##      8       31.7084             nan     0.0500    2.7912
##      9       29.4480             nan     0.0500    1.8641
##     10       27.3565             nan     0.0500    2.0022
##     20       14.2739             nan     0.0500    0.7857
##     40        6.1140             nan     0.0500    0.1950
##     60        4.2708             nan     0.0500    0.0132
##     80        3.6362             nan     0.0500   -0.0039
##    100        3.3328             nan     0.0500   -0.0344
##    120        3.1261             nan     0.0500   -0.0336
##    140        2.9107             nan     0.0500   -0.0101
##    160        2.7323             nan     0.0500   -0.0076
##    180        2.5784             nan     0.0500   -0.0225
##    200        2.4596             nan     0.0500   -0.0221
##    220        2.3346             nan     0.0500   -0.0138
##    240        2.2000             nan     0.0500   -0.0090
##    260        2.1065             nan     0.0500   -0.0087
##    280        2.0111             nan     0.0500   -0.0090
##    300        1.9241             nan     0.0500   -0.0158
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.0596             nan     0.0500    4.8970
##      2       50.6709             nan     0.0500    4.6436
##      3       46.7853             nan     0.0500    3.8941
##      4       43.1507             nan     0.0500    3.7033
##      5       39.8694             nan     0.0500    3.5110
##      6       36.8854             nan     0.0500    3.0157
##      7       34.2227             nan     0.0500    2.3791
##      8       31.7756             nan     0.0500    2.4047
##      9       29.4824             nan     0.0500    2.2494
##     10       27.4525             nan     0.0500    1.8720
##     20       14.4102             nan     0.0500    0.7882
##     40        6.2311             nan     0.0500    0.1794
##     60        4.3526             nan     0.0500    0.0146
##     80        3.7797             nan     0.0500   -0.0049
##    100        3.4830             nan     0.0500   -0.0129
##    120        3.3334             nan     0.0500   -0.0170
##    140        3.1593             nan     0.0500   -0.0156
##    160        3.0000             nan     0.0500   -0.0076
##    180        2.8621             nan     0.0500   -0.0170
##    200        2.7677             nan     0.0500   -0.0134
##    220        2.6649             nan     0.0500   -0.0138
##    240        2.5697             nan     0.0500   -0.0183
##    260        2.4969             nan     0.0500   -0.0226
##    280        2.4077             nan     0.0500   -0.0170
##    300        2.3356             nan     0.0500   -0.0114
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.0833             nan     0.0500    4.8117
##      2       50.6656             nan     0.0500    4.2385
##      3       46.7431             nan     0.0500    3.8701
##      4       42.9569             nan     0.0500    3.5938
##      5       39.7451             nan     0.0500    3.1308
##      6       36.8169             nan     0.0500    3.0587
##      7       34.0997             nan     0.0500    2.5170
##      8       31.5950             nan     0.0500    2.2671
##      9       29.3643             nan     0.0500    2.1708
##     10       27.3067             nan     0.0500    1.9808
##     20       14.4779             nan     0.0500    0.7930
##     40        6.3575             nan     0.0500    0.1344
##     60        4.4701             nan     0.0500    0.0154
##     80        3.9765             nan     0.0500   -0.0194
##    100        3.7571             nan     0.0500   -0.0311
##    120        3.5849             nan     0.0500   -0.0210
##    140        3.4075             nan     0.0500   -0.0103
##    160        3.2624             nan     0.0500   -0.0202
##    180        3.1568             nan     0.0500   -0.0070
##    200        3.0477             nan     0.0500   -0.0056
##    220        2.9617             nan     0.0500   -0.0117
##    240        2.8730             nan     0.0500   -0.0242
##    260        2.7891             nan     0.0500   -0.0168
##    280        2.7071             nan     0.0500   -0.0239
##    300        2.6284             nan     0.0500   -0.0136
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.8492             nan     0.0500    4.5112
##      2       50.0878             nan     0.0500    4.1064
##      3       45.6928             nan     0.0500    3.4714
##      4       41.9608             nan     0.0500    3.0862
##      5       38.5289             nan     0.0500    3.8358
##      6       35.3583             nan     0.0500    3.2631
##      7       32.4365             nan     0.0500    3.0389
##      8       29.9451             nan     0.0500    2.1873
##      9       27.6902             nan     0.0500    2.3201
##     10       25.6727             nan     0.0500    1.8012
##     20       12.6098             nan     0.0500    0.6920
##     40        5.0594             nan     0.0500    0.1118
##     60        3.4778             nan     0.0500    0.0052
##     80        2.9385             nan     0.0500   -0.0361
##    100        2.5817             nan     0.0500   -0.0057
##    120        2.3473             nan     0.0500   -0.0182
##    140        2.1504             nan     0.0500   -0.0235
##    160        1.9689             nan     0.0500   -0.0140
##    180        1.8034             nan     0.0500   -0.0080
##    200        1.6642             nan     0.0500   -0.0089
##    220        1.5486             nan     0.0500   -0.0105
##    240        1.4579             nan     0.0500   -0.0100
##    260        1.3498             nan     0.0500   -0.0079
##    280        1.2615             nan     0.0500   -0.0116
##    300        1.1803             nan     0.0500   -0.0142
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.6374             nan     0.0500    5.1688
##      2       49.8232             nan     0.0500    4.8748
##      3       45.9219             nan     0.0500    3.8021
##      4       42.0953             nan     0.0500    3.7668
##      5       38.6995             nan     0.0500    3.4668
##      6       35.6279             nan     0.0500    3.2604
##      7       32.8248             nan     0.0500    2.7440
##      8       30.2593             nan     0.0500    2.3873
##      9       27.8219             nan     0.0500    2.0921
##     10       25.7640             nan     0.0500    2.2734
##     20       12.8661             nan     0.0500    0.8644
##     40        5.3059             nan     0.0500    0.0879
##     60        3.8528             nan     0.0500    0.0108
##     80        3.3234             nan     0.0500   -0.0208
##    100        3.0574             nan     0.0500   -0.0395
##    120        2.8417             nan     0.0500   -0.0215
##    140        2.6546             nan     0.0500   -0.0343
##    160        2.4573             nan     0.0500   -0.0258
##    180        2.2995             nan     0.0500   -0.0151
##    200        2.1688             nan     0.0500   -0.0176
##    220        2.0336             nan     0.0500   -0.0122
##    240        1.9357             nan     0.0500   -0.0201
##    260        1.8412             nan     0.0500   -0.0160
##    280        1.7303             nan     0.0500   -0.0092
##    300        1.6556             nan     0.0500   -0.0152
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.0788             nan     0.0500    4.7070
##      2       50.7048             nan     0.0500    4.2124
##      3       46.5665             nan     0.0500    3.8875
##      4       42.6249             nan     0.0500    3.9539
##      5       39.2931             nan     0.0500    3.2314
##      6       36.2105             nan     0.0500    2.8375
##      7       33.1810             nan     0.0500    2.7027
##      8       30.8120             nan     0.0500    2.3923
##      9       28.5429             nan     0.0500    2.4052
##     10       26.1687             nan     0.0500    1.9534
##     20       13.0188             nan     0.0500    0.8343
##     40        5.5594             nan     0.0500    0.1089
##     60        4.1702             nan     0.0500   -0.0218
##     80        3.7304             nan     0.0500   -0.0354
##    100        3.4427             nan     0.0500   -0.0104
##    120        3.2040             nan     0.0500   -0.0182
##    140        3.0432             nan     0.0500   -0.0142
##    160        2.8790             nan     0.0500   -0.0158
##    180        2.7583             nan     0.0500   -0.0244
##    200        2.6451             nan     0.0500   -0.0167
##    220        2.5285             nan     0.0500   -0.0052
##    240        2.4306             nan     0.0500   -0.0070
##    260        2.3596             nan     0.0500   -0.0139
##    280        2.2816             nan     0.0500   -0.0134
##    300        2.1927             nan     0.0500   -0.0092
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.5281             nan     0.1000    7.2479
##      2       47.8149             nan     0.1000    5.5422
##      3       42.4283             nan     0.1000    4.6154
##      4       37.8311             nan     0.1000    3.8128
##      5       34.1432             nan     0.1000    3.2938
##      6       30.9308             nan     0.1000    3.0563
##      7       28.1967             nan     0.1000    2.8445
##      8       25.7157             nan     0.1000    2.2590
##      9       23.5325             nan     0.1000    2.2328
##     10       21.5697             nan     0.1000    1.5910
##     20       11.4103             nan     0.1000    0.4502
##     40        5.8908             nan     0.1000    0.0649
##     60        4.7470             nan     0.1000    0.0244
##     80        4.4876             nan     0.1000   -0.0195
##    100        4.2819             nan     0.1000   -0.0303
##    120        4.1210             nan     0.1000   -0.0486
##    140        4.0010             nan     0.1000   -0.0140
##    160        3.9178             nan     0.1000   -0.0038
##    180        3.8191             nan     0.1000   -0.0033
##    200        3.7323             nan     0.1000   -0.0178
##    220        3.6466             nan     0.1000   -0.0273
##    240        3.5952             nan     0.1000   -0.0385
##    260        3.5568             nan     0.1000   -0.0101
##    280        3.4850             nan     0.1000   -0.0117
##    300        3.4171             nan     0.1000   -0.0290
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.4688             nan     0.1000    6.8355
##      2       46.7668             nan     0.1000    5.6443
##      3       42.0435             nan     0.1000    3.7916
##      4       37.6993             nan     0.1000    4.1359
##      5       33.9098             nan     0.1000    3.6237
##      6       30.6766             nan     0.1000    3.0740
##      7       28.0555             nan     0.1000    2.6132
##      8       25.7755             nan     0.1000    2.3319
##      9       23.5610             nan     0.1000    2.1597
##     10       21.7994             nan     0.1000    1.4807
##     20       11.2909             nan     0.1000    0.4523
##     40        5.7716             nan     0.1000    0.1114
##     60        4.6718             nan     0.1000    0.0014
##     80        4.4266             nan     0.1000   -0.0225
##    100        4.2606             nan     0.1000   -0.0082
##    120        4.1296             nan     0.1000   -0.0291
##    140        4.0137             nan     0.1000   -0.0248
##    160        3.9415             nan     0.1000   -0.0137
##    180        3.8712             nan     0.1000   -0.0206
##    200        3.7952             nan     0.1000   -0.0216
##    220        3.7414             nan     0.1000   -0.0157
##    240        3.6685             nan     0.1000   -0.0029
##    260        3.6078             nan     0.1000   -0.0007
##    280        3.5374             nan     0.1000   -0.0101
##    300        3.4830             nan     0.1000   -0.0273
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.5100             nan     0.1000    7.3293
##      2       47.0078             nan     0.1000    4.4970
##      3       41.5680             nan     0.1000    5.0181
##      4       37.8943             nan     0.1000    2.9780
##      5       34.0442             nan     0.1000    3.8521
##      6       30.6512             nan     0.1000    3.3069
##      7       28.0318             nan     0.1000    2.5961
##      8       25.5893             nan     0.1000    2.3818
##      9       23.6862             nan     0.1000    1.9288
##     10       21.7993             nan     0.1000    1.6415
##     20       11.1761             nan     0.1000    0.4423
##     40        5.8680             nan     0.1000    0.0417
##     60        4.8751             nan     0.1000    0.0218
##     80        4.6029             nan     0.1000   -0.0001
##    100        4.4036             nan     0.1000   -0.0353
##    120        4.3228             nan     0.1000   -0.0226
##    140        4.2011             nan     0.1000   -0.0193
##    160        4.0845             nan     0.1000   -0.0070
##    180        4.0007             nan     0.1000   -0.0113
##    200        3.9276             nan     0.1000   -0.0088
##    220        3.8578             nan     0.1000   -0.0142
##    240        3.7646             nan     0.1000   -0.0167
##    260        3.7279             nan     0.1000   -0.0070
##    280        3.6619             nan     0.1000   -0.0199
##    300        3.6180             nan     0.1000   -0.0356
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.5569             nan     0.1000    8.9914
##      2       42.9705             nan     0.1000    8.5212
##      3       36.2586             nan     0.1000    6.2291
##      4       30.9603             nan     0.1000    4.9982
##      5       26.5420             nan     0.1000    3.8534
##      6       23.2380             nan     0.1000    3.4850
##      7       20.2497             nan     0.1000    2.9528
##      8       17.7105             nan     0.1000    2.4129
##      9       15.7135             nan     0.1000    1.7885
##     10       13.9571             nan     0.1000    1.5724
##     20        6.1086             nan     0.1000    0.2061
##     40        3.6734             nan     0.1000   -0.0460
##     60        3.1664             nan     0.1000   -0.0425
##     80        2.7902             nan     0.1000   -0.0377
##    100        2.4625             nan     0.1000   -0.0293
##    120        2.2336             nan     0.1000   -0.0352
##    140        2.0254             nan     0.1000   -0.0328
##    160        1.8481             nan     0.1000   -0.0248
##    180        1.7119             nan     0.1000   -0.0222
##    200        1.5902             nan     0.1000   -0.0182
##    220        1.4940             nan     0.1000   -0.0340
##    240        1.3882             nan     0.1000   -0.0247
##    260        1.3119             nan     0.1000   -0.0136
##    280        1.2483             nan     0.1000   -0.0200
##    300        1.1720             nan     0.1000   -0.0120
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.3462             nan     0.1000   10.0493
##      2       42.8392             nan     0.1000    7.9173
##      3       36.4507             nan     0.1000    6.1877
##      4       31.0251             nan     0.1000    5.7378
##      5       26.7595             nan     0.1000    4.2033
##      6       23.2069             nan     0.1000    3.7299
##      7       20.3183             nan     0.1000    2.2430
##      8       17.9982             nan     0.1000    2.5113
##      9       15.9327             nan     0.1000    1.9216
##     10       13.9819             nan     0.1000    1.9531
##     20        6.0913             nan     0.1000    0.3516
##     40        3.8748             nan     0.1000   -0.0575
##     60        3.3743             nan     0.1000   -0.0592
##     80        3.0458             nan     0.1000   -0.0129
##    100        2.8265             nan     0.1000   -0.0392
##    120        2.6353             nan     0.1000   -0.0324
##    140        2.4789             nan     0.1000   -0.0281
##    160        2.3258             nan     0.1000   -0.0157
##    180        2.1810             nan     0.1000   -0.0343
##    200        2.0515             nan     0.1000   -0.0331
##    220        1.9500             nan     0.1000   -0.0103
##    240        1.8550             nan     0.1000   -0.0134
##    260        1.7561             nan     0.1000   -0.0267
##    280        1.6364             nan     0.1000   -0.0169
##    300        1.5617             nan     0.1000   -0.0173
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.6671             nan     0.1000    9.9589
##      2       42.7561             nan     0.1000    7.9993
##      3       36.2836             nan     0.1000    6.1796
##      4       31.1824             nan     0.1000    5.3339
##      5       26.8244             nan     0.1000    3.8962
##      6       23.0563             nan     0.1000    3.4314
##      7       20.3664             nan     0.1000    2.1878
##      8       17.9298             nan     0.1000    2.2807
##      9       15.8261             nan     0.1000    2.1278
##     10       13.9396             nan     0.1000    1.6152
##     20        6.2854             nan     0.1000    0.3411
##     40        4.0349             nan     0.1000   -0.0027
##     60        3.5577             nan     0.1000   -0.0394
##     80        3.3283             nan     0.1000   -0.0447
##    100        3.1081             nan     0.1000   -0.0310
##    120        2.9438             nan     0.1000   -0.0208
##    140        2.7790             nan     0.1000   -0.0228
##    160        2.6710             nan     0.1000   -0.0521
##    180        2.5232             nan     0.1000   -0.0441
##    200        2.4025             nan     0.1000   -0.0267
##    220        2.3108             nan     0.1000   -0.0054
##    240        2.2322             nan     0.1000   -0.0334
##    260        2.1119             nan     0.1000   -0.0159
##    280        2.0132             nan     0.1000   -0.0230
##    300        1.9296             nan     0.1000   -0.0186
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.0617             nan     0.1000   10.7192
##      2       41.9580             nan     0.1000    7.5755
##      3       35.6701             nan     0.1000    6.1662
##      4       30.5153             nan     0.1000    4.6316
##      5       25.9751             nan     0.1000    4.5299
##      6       22.3096             nan     0.1000    3.3097
##      7       18.9979             nan     0.1000    2.9207
##      8       16.4853             nan     0.1000    2.2905
##      9       14.1806             nan     0.1000    2.1583
##     10       12.5830             nan     0.1000    1.6320
##     20        5.1973             nan     0.1000    0.1760
##     40        3.1049             nan     0.1000   -0.0290
##     60        2.4424             nan     0.1000   -0.0539
##     80        2.0284             nan     0.1000   -0.0248
##    100        1.7115             nan     0.1000   -0.0156
##    120        1.4794             nan     0.1000   -0.0230
##    140        1.2833             nan     0.1000   -0.0102
##    160        1.1413             nan     0.1000   -0.0221
##    180        1.0078             nan     0.1000   -0.0131
##    200        0.8872             nan     0.1000   -0.0111
##    220        0.8038             nan     0.1000   -0.0132
##    240        0.7118             nan     0.1000   -0.0111
##    260        0.6464             nan     0.1000   -0.0119
##    280        0.5772             nan     0.1000   -0.0092
##    300        0.5311             nan     0.1000   -0.0098
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.3042             nan     0.1000   10.2468
##      2       42.2105             nan     0.1000    7.9157
##      3       35.3382             nan     0.1000    6.5465
##      4       29.6980             nan     0.1000    5.4504
##      5       25.4972             nan     0.1000    4.1565
##      6       22.0284             nan     0.1000    3.3164
##      7       18.9599             nan     0.1000    2.7861
##      8       16.4990             nan     0.1000    2.4939
##      9       14.3586             nan     0.1000    2.1044
##     10       12.4830             nan     0.1000    1.6987
##     20        5.0833             nan     0.1000    0.1616
##     40        3.3004             nan     0.1000   -0.0661
##     60        2.7950             nan     0.1000   -0.0718
##     80        2.4609             nan     0.1000   -0.0481
##    100        2.1921             nan     0.1000   -0.0158
##    120        1.9888             nan     0.1000   -0.0496
##    140        1.8065             nan     0.1000   -0.0207
##    160        1.6749             nan     0.1000   -0.0121
##    180        1.5159             nan     0.1000   -0.0223
##    200        1.3948             nan     0.1000   -0.0241
##    220        1.3063             nan     0.1000   -0.0282
##    240        1.1998             nan     0.1000   -0.0258
##    260        1.1139             nan     0.1000   -0.0114
##    280        1.0310             nan     0.1000   -0.0140
##    300        0.9670             nan     0.1000   -0.0196
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.2072             nan     0.1000   10.7663
##      2       42.1874             nan     0.1000    8.2551
##      3       35.5826             nan     0.1000    6.8193
##      4       30.0732             nan     0.1000    5.0494
##      5       25.3116             nan     0.1000    3.8473
##      6       21.6925             nan     0.1000    3.3290
##      7       18.6747             nan     0.1000    2.8151
##      8       16.1471             nan     0.1000    2.4604
##      9       13.8891             nan     0.1000    2.2895
##     10       12.1598             nan     0.1000    1.6660
##     20        5.2250             nan     0.1000    0.2167
##     40        3.6210             nan     0.1000   -0.0366
##     60        3.1802             nan     0.1000   -0.0213
##     80        2.8731             nan     0.1000   -0.0365
##    100        2.6212             nan     0.1000   -0.0467
##    120        2.4340             nan     0.1000   -0.0312
##    140        2.2588             nan     0.1000   -0.0316
##    160        2.0799             nan     0.1000   -0.0425
##    180        1.9716             nan     0.1000   -0.0153
##    200        1.8239             nan     0.1000   -0.0262
##    220        1.7260             nan     0.1000   -0.0316
##    240        1.6443             nan     0.1000   -0.0459
##    260        1.5428             nan     0.1000   -0.0245
##    280        1.4481             nan     0.1000   -0.0252
##    300        1.3750             nan     0.1000   -0.0236
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.7660             nan     0.0100    0.7927
##      2       59.9760             nan     0.0100    0.7477
##      3       59.2460             nan     0.0100    0.7661
##      4       58.5057             nan     0.0100    0.7125
##      5       57.7785             nan     0.0100    0.6982
##      6       57.0185             nan     0.0100    0.6674
##      7       56.2991             nan     0.0100    0.6669
##      8       55.6880             nan     0.0100    0.6322
##      9       55.0246             nan     0.0100    0.6155
##     10       54.3761             nan     0.0100    0.6360
##     20       48.7411             nan     0.0100    0.4891
##     40       39.4304             nan     0.0100    0.3750
##     60       32.2577             nan     0.0100    0.3153
##     80       26.7796             nan     0.0100    0.2220
##    100       22.7368             nan     0.0100    0.1573
##    120       19.5698             nan     0.0100    0.1236
##    140       17.0398             nan     0.0100    0.0929
##    160       15.0214             nan     0.0100    0.0786
##    180       13.4291             nan     0.0100    0.0611
##    200       12.0654             nan     0.0100    0.0583
##    220       10.9251             nan     0.0100    0.0325
##    240        9.9583             nan     0.0100    0.0414
##    260        9.1621             nan     0.0100    0.0304
##    280        8.4568             nan     0.0100    0.0284
##    300        7.8666             nan     0.0100    0.0126
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.7870             nan     0.0100    0.7521
##      2       59.9866             nan     0.0100    0.7324
##      3       59.2573             nan     0.0100    0.7847
##      4       58.5858             nan     0.0100    0.7678
##      5       57.8359             nan     0.0100    0.7086
##      6       57.1980             nan     0.0100    0.6928
##      7       56.5639             nan     0.0100    0.6651
##      8       55.8827             nan     0.0100    0.6671
##      9       55.2338             nan     0.0100    0.6655
##     10       54.5715             nan     0.0100    0.6858
##     20       48.6301             nan     0.0100    0.5175
##     40       39.4264             nan     0.0100    0.3907
##     60       32.2260             nan     0.0100    0.2737
##     80       26.8744             nan     0.0100    0.2050
##    100       22.8219             nan     0.0100    0.1605
##    120       19.6324             nan     0.0100    0.1127
##    140       17.0758             nan     0.0100    0.1062
##    160       15.0522             nan     0.0100    0.0783
##    180       13.4349             nan     0.0100    0.0576
##    200       12.0865             nan     0.0100    0.0231
##    220       10.9397             nan     0.0100    0.0408
##    240        9.9785             nan     0.0100    0.0353
##    260        9.1596             nan     0.0100    0.0242
##    280        8.5025             nan     0.0100    0.0225
##    300        7.9023             nan     0.0100    0.0253
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.8056             nan     0.0100    0.7570
##      2       59.9650             nan     0.0100    0.7216
##      3       59.2376             nan     0.0100    0.6620
##      4       58.5428             nan     0.0100    0.7071
##      5       57.8593             nan     0.0100    0.6982
##      6       57.1008             nan     0.0100    0.7085
##      7       56.4487             nan     0.0100    0.6525
##      8       55.7938             nan     0.0100    0.6292
##      9       55.1709             nan     0.0100    0.6243
##     10       54.4801             nan     0.0100    0.6401
##     20       48.7284             nan     0.0100    0.5626
##     40       39.4472             nan     0.0100    0.3906
##     60       32.5514             nan     0.0100    0.2997
##     80       27.1401             nan     0.0100    0.2148
##    100       22.9807             nan     0.0100    0.1464
##    120       19.8094             nan     0.0100    0.1218
##    140       17.2751             nan     0.0100    0.1043
##    160       15.2594             nan     0.0100    0.0786
##    180       13.5752             nan     0.0100    0.0567
##    200       12.1654             nan     0.0100    0.0432
##    220       11.0535             nan     0.0100    0.0412
##    240       10.1118             nan     0.0100    0.0383
##    260        9.3100             nan     0.0100    0.0279
##    280        8.6686             nan     0.0100    0.0156
##    300        8.0720             nan     0.0100    0.0240
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.6128             nan     0.0100    1.0022
##      2       59.6328             nan     0.0100    0.9715
##      3       58.6518             nan     0.0100    0.8832
##      4       57.6960             nan     0.0100    0.9816
##      5       56.8173             nan     0.0100    0.9529
##      6       55.9287             nan     0.0100    0.7488
##      7       55.0415             nan     0.0100    0.8416
##      8       54.1264             nan     0.0100    0.8460
##      9       53.2894             nan     0.0100    0.8406
##     10       52.4748             nan     0.0100    0.9753
##     20       44.7402             nan     0.0100    0.7383
##     40       33.0959             nan     0.0100    0.4822
##     60       24.9887             nan     0.0100    0.3277
##     80       19.1918             nan     0.0100    0.2214
##    100       15.1476             nan     0.0100    0.1284
##    120       12.2353             nan     0.0100    0.0843
##    140       10.0538             nan     0.0100    0.0946
##    160        8.4775             nan     0.0100    0.0641
##    180        7.2453             nan     0.0100    0.0427
##    200        6.3517             nan     0.0100    0.0266
##    220        5.6713             nan     0.0100    0.0245
##    240        5.1686             nan     0.0100    0.0153
##    260        4.7650             nan     0.0100    0.0105
##    280        4.4646             nan     0.0100    0.0067
##    300        4.2264             nan     0.0100    0.0029
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.5423             nan     0.0100    1.0307
##      2       59.5733             nan     0.0100    0.8742
##      3       58.5631             nan     0.0100    0.9935
##      4       57.6165             nan     0.0100    0.9033
##      5       56.7044             nan     0.0100    0.9568
##      6       55.7388             nan     0.0100    0.9210
##      7       54.8304             nan     0.0100    0.8119
##      8       53.9934             nan     0.0100    0.9096
##      9       53.1347             nan     0.0100    0.8593
##     10       52.3063             nan     0.0100    0.8568
##     20       44.8563             nan     0.0100    0.7231
##     40       32.9925             nan     0.0100    0.5209
##     60       24.9360             nan     0.0100    0.3016
##     80       19.1590             nan     0.0100    0.2231
##    100       15.1536             nan     0.0100    0.1545
##    120       12.2133             nan     0.0100    0.1124
##    140       10.0810             nan     0.0100    0.0798
##    160        8.4968             nan     0.0100    0.0561
##    180        7.3302             nan     0.0100    0.0431
##    200        6.4736             nan     0.0100    0.0340
##    220        5.8020             nan     0.0100    0.0259
##    240        5.2909             nan     0.0100    0.0189
##    260        4.9316             nan     0.0100    0.0145
##    280        4.6331             nan     0.0100    0.0074
##    300        4.3976             nan     0.0100    0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.5356             nan     0.0100    1.0743
##      2       59.5432             nan     0.0100    0.9617
##      3       58.6154             nan     0.0100    0.8984
##      4       57.6770             nan     0.0100    0.8789
##      5       56.7191             nan     0.0100    0.8860
##      6       55.8204             nan     0.0100    0.8711
##      7       54.8951             nan     0.0100    0.8866
##      8       54.0114             nan     0.0100    0.8630
##      9       53.1628             nan     0.0100    0.8181
##     10       52.3114             nan     0.0100    0.7424
##     20       44.6154             nan     0.0100    0.7551
##     40       33.0485             nan     0.0100    0.4651
##     60       24.9674             nan     0.0100    0.3414
##     80       19.2844             nan     0.0100    0.2349
##    100       15.2722             nan     0.0100    0.1593
##    120       12.3756             nan     0.0100    0.0879
##    140       10.2981             nan     0.0100    0.0777
##    160        8.7805             nan     0.0100    0.0578
##    180        7.6221             nan     0.0100    0.0443
##    200        6.7545             nan     0.0100    0.0296
##    220        6.1149             nan     0.0100    0.0198
##    240        5.6238             nan     0.0100    0.0135
##    260        5.2662             nan     0.0100    0.0097
##    280        4.9687             nan     0.0100    0.0059
##    300        4.7149             nan     0.0100    0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.5280             nan     0.0100    1.0017
##      2       59.4748             nan     0.0100    0.9913
##      3       58.3938             nan     0.0100    0.9527
##      4       57.4358             nan     0.0100    1.0419
##      5       56.3800             nan     0.0100    0.9905
##      6       55.4186             nan     0.0100    0.9519
##      7       54.4388             nan     0.0100    0.9300
##      8       53.5170             nan     0.0100    0.8389
##      9       52.5851             nan     0.0100    0.8512
##     10       51.7152             nan     0.0100    0.9048
##     20       43.6651             nan     0.0100    0.7197
##     40       31.5091             nan     0.0100    0.4966
##     60       23.0466             nan     0.0100    0.3513
##     80       17.2732             nan     0.0100    0.2402
##    100       13.2369             nan     0.0100    0.1814
##    120       10.3737             nan     0.0100    0.0978
##    140        8.3112             nan     0.0100    0.0581
##    160        6.9222             nan     0.0100    0.0510
##    180        5.9136             nan     0.0100    0.0315
##    200        5.1603             nan     0.0100    0.0218
##    220        4.6269             nan     0.0100    0.0170
##    240        4.1995             nan     0.0100    0.0151
##    260        3.8660             nan     0.0100    0.0042
##    280        3.6156             nan     0.0100   -0.0011
##    300        3.4135             nan     0.0100    0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.4604             nan     0.0100    0.9130
##      2       59.4163             nan     0.0100    1.0636
##      3       58.3742             nan     0.0100    0.9256
##      4       57.3522             nan     0.0100    0.9604
##      5       56.3652             nan     0.0100    1.0048
##      6       55.4290             nan     0.0100    0.8571
##      7       54.4483             nan     0.0100    0.9713
##      8       53.5343             nan     0.0100    0.8623
##      9       52.6204             nan     0.0100    0.8903
##     10       51.7416             nan     0.0100    0.9246
##     20       43.7414             nan     0.0100    0.7379
##     40       31.4345             nan     0.0100    0.4721
##     60       23.0673             nan     0.0100    0.3276
##     80       17.1852             nan     0.0100    0.1959
##    100       13.2600             nan     0.0100    0.1481
##    120       10.4380             nan     0.0100    0.1210
##    140        8.5035             nan     0.0100    0.0816
##    160        7.1124             nan     0.0100    0.0507
##    180        6.0979             nan     0.0100    0.0300
##    200        5.3779             nan     0.0100    0.0110
##    220        4.8405             nan     0.0100    0.0163
##    240        4.4333             nan     0.0100    0.0071
##    260        4.1369             nan     0.0100    0.0021
##    280        3.9018             nan     0.0100   -0.0008
##    300        3.7187             nan     0.0100   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.4956             nan     0.0100    1.1134
##      2       59.4575             nan     0.0100    0.9308
##      3       58.4299             nan     0.0100    0.9929
##      4       57.4287             nan     0.0100    0.8407
##      5       56.4436             nan     0.0100    1.0395
##      6       55.4575             nan     0.0100    0.9833
##      7       54.5460             nan     0.0100    0.9485
##      8       53.6878             nan     0.0100    0.9528
##      9       52.7938             nan     0.0100    0.9172
##     10       51.9040             nan     0.0100    0.8074
##     20       43.8624             nan     0.0100    0.7445
##     40       31.8083             nan     0.0100    0.4275
##     60       23.4099             nan     0.0100    0.2895
##     80       17.5890             nan     0.0100    0.2429
##    100       13.6808             nan     0.0100    0.1471
##    120       10.8663             nan     0.0100    0.0977
##    140        8.8851             nan     0.0100    0.0708
##    160        7.4761             nan     0.0100    0.0551
##    180        6.4844             nan     0.0100    0.0395
##    200        5.7690             nan     0.0100    0.0272
##    220        5.2517             nan     0.0100    0.0132
##    240        4.8505             nan     0.0100    0.0116
##    260        4.5769             nan     0.0100    0.0029
##    280        4.3490             nan     0.0100    0.0079
##    300        4.1635             nan     0.0100   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.7015             nan     0.0500    3.4982
##      2       54.2418             nan     0.0500    3.1776
##      3       51.0148             nan     0.0500    2.7234
##      4       48.1848             nan     0.0500    2.8085
##      5       45.9234             nan     0.0500    2.3217
##      6       43.5831             nan     0.0500    2.4684
##      7       41.5490             nan     0.0500    1.7567
##      8       39.5428             nan     0.0500    1.8231
##      9       37.6141             nan     0.0500    2.1494
##     10       35.7790             nan     0.0500    1.7716
##     20       22.9533             nan     0.0500    0.9134
##     40       12.2589             nan     0.0500    0.2651
##     60        8.1055             nan     0.0500    0.1218
##     80        6.0937             nan     0.0500    0.0217
##    100        5.2074             nan     0.0500   -0.0067
##    120        4.7190             nan     0.0500   -0.0083
##    140        4.4785             nan     0.0500   -0.0069
##    160        4.3330             nan     0.0500   -0.0086
##    180        4.2576             nan     0.0500   -0.0184
##    200        4.1562             nan     0.0500   -0.0269
##    220        4.0912             nan     0.0500   -0.0132
##    240        4.0271             nan     0.0500   -0.0104
##    260        3.9630             nan     0.0500   -0.0184
##    280        3.9027             nan     0.0500   -0.0027
##    300        3.8374             nan     0.0500   -0.0206
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.5794             nan     0.0500    3.7992
##      2       54.3230             nan     0.0500    3.2092
##      3       50.9122             nan     0.0500    3.2266
##      4       48.0505             nan     0.0500    2.5112
##      5       45.4481             nan     0.0500    2.2672
##      6       43.0324             nan     0.0500    2.2788
##      7       40.7323             nan     0.0500    1.9265
##      8       38.7387             nan     0.0500    1.9601
##      9       36.7217             nan     0.0500    1.9000
##     10       34.8995             nan     0.0500    1.6411
##     20       22.4937             nan     0.0500    0.9089
##     40       11.8917             nan     0.0500    0.2149
##     60        7.7633             nan     0.0500    0.1225
##     80        5.9714             nan     0.0500    0.0120
##    100        5.0798             nan     0.0500   -0.0101
##    120        4.7007             nan     0.0500    0.0081
##    140        4.4883             nan     0.0500   -0.0114
##    160        4.3417             nan     0.0500   -0.0147
##    180        4.2465             nan     0.0500   -0.0115
##    200        4.1704             nan     0.0500   -0.0351
##    220        4.1005             nan     0.0500   -0.0115
##    240        4.0413             nan     0.0500   -0.0055
##    260        3.9864             nan     0.0500   -0.0031
##    280        3.9366             nan     0.0500   -0.0141
##    300        3.8887             nan     0.0500   -0.0218
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.7586             nan     0.0500    3.5394
##      2       54.2993             nan     0.0500    3.7989
##      3       50.9941             nan     0.0500    2.9821
##      4       48.2152             nan     0.0500    2.4799
##      5       45.5350             nan     0.0500    2.4753
##      6       43.1732             nan     0.0500    2.1965
##      7       40.9369             nan     0.0500    2.2222
##      8       38.7347             nan     0.0500    2.0183
##      9       37.0822             nan     0.0500    1.7347
##     10       35.2522             nan     0.0500    1.6259
##     20       22.4820             nan     0.0500    0.8752
##     40       11.9974             nan     0.0500    0.2520
##     60        8.0188             nan     0.0500    0.0830
##     80        6.2073             nan     0.0500    0.0574
##    100        5.3636             nan     0.0500    0.0242
##    120        4.9926             nan     0.0500    0.0054
##    140        4.7710             nan     0.0500   -0.0054
##    160        4.6372             nan     0.0500   -0.0150
##    180        4.4956             nan     0.0500   -0.0032
##    200        4.4152             nan     0.0500   -0.0095
##    220        4.3268             nan     0.0500   -0.0298
##    240        4.2491             nan     0.0500   -0.0035
##    260        4.1822             nan     0.0500   -0.0180
##    280        4.1274             nan     0.0500   -0.0079
##    300        4.0693             nan     0.0500   -0.0078
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.7125             nan     0.0500    4.8834
##      2       52.0471             nan     0.0500    4.5408
##      3       48.0224             nan     0.0500    3.6153
##      4       44.5774             nan     0.0500    3.6362
##      5       41.1548             nan     0.0500    3.6088
##      6       38.1180             nan     0.0500    3.0381
##      7       35.3822             nan     0.0500    2.8435
##      8       32.8657             nan     0.0500    2.2436
##      9       30.5260             nan     0.0500    2.3342
##     10       28.4272             nan     0.0500    1.9290
##     20       14.7456             nan     0.0500    0.7583
##     40        6.2283             nan     0.0500    0.1508
##     60        4.2450             nan     0.0500    0.0093
##     80        3.5616             nan     0.0500   -0.0029
##    100        3.1795             nan     0.0500   -0.0228
##    120        2.9053             nan     0.0500   -0.0146
##    140        2.6717             nan     0.0500   -0.0094
##    160        2.5057             nan     0.0500   -0.0022
##    180        2.3555             nan     0.0500   -0.0145
##    200        2.2091             nan     0.0500   -0.0110
##    220        2.0794             nan     0.0500   -0.0099
##    240        1.9680             nan     0.0500   -0.0180
##    260        1.8718             nan     0.0500   -0.0193
##    280        1.7793             nan     0.0500   -0.0071
##    300        1.7130             nan     0.0500   -0.0149
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.7490             nan     0.0500    4.6916
##      2       52.3650             nan     0.0500    4.6628
##      3       48.4174             nan     0.0500    3.9909
##      4       44.7935             nan     0.0500    3.4565
##      5       41.4009             nan     0.0500    3.0197
##      6       38.2488             nan     0.0500    2.7600
##      7       35.3945             nan     0.0500    2.7726
##      8       32.6785             nan     0.0500    2.6216
##      9       30.4299             nan     0.0500    2.2168
##     10       28.2481             nan     0.0500    2.0930
##     20       14.7523             nan     0.0500    0.8113
##     40        6.4072             nan     0.0500    0.1383
##     60        4.3777             nan     0.0500   -0.0053
##     80        3.7216             nan     0.0500   -0.0066
##    100        3.3925             nan     0.0500   -0.0137
##    120        3.1470             nan     0.0500   -0.0176
##    140        2.9600             nan     0.0500   -0.0195
##    160        2.8231             nan     0.0500   -0.0119
##    180        2.6784             nan     0.0500   -0.0159
##    200        2.5609             nan     0.0500   -0.0174
##    220        2.4551             nan     0.0500   -0.0076
##    240        2.3622             nan     0.0500   -0.0143
##    260        2.2791             nan     0.0500   -0.0149
##    280        2.1983             nan     0.0500   -0.0199
##    300        2.1106             nan     0.0500   -0.0137
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.8504             nan     0.0500    5.5487
##      2       52.3049             nan     0.0500    4.3477
##      3       48.1635             nan     0.0500    4.0110
##      4       44.4085             nan     0.0500    3.7169
##      5       41.2354             nan     0.0500    3.0825
##      6       38.0796             nan     0.0500    2.9684
##      7       35.1033             nan     0.0500    2.6028
##      8       32.6581             nan     0.0500    2.4002
##      9       30.3600             nan     0.0500    2.2336
##     10       28.4114             nan     0.0500    2.1210
##     20       15.0378             nan     0.0500    0.7062
##     40        6.8280             nan     0.0500    0.1544
##     60        4.8615             nan     0.0500    0.0122
##     80        4.2513             nan     0.0500   -0.0209
##    100        3.8814             nan     0.0500   -0.0027
##    120        3.6592             nan     0.0500   -0.0246
##    140        3.4690             nan     0.0500   -0.0126
##    160        3.2878             nan     0.0500    0.0015
##    180        3.1510             nan     0.0500   -0.0087
##    200        3.0242             nan     0.0500   -0.0176
##    220        2.9191             nan     0.0500   -0.0308
##    240        2.8278             nan     0.0500   -0.0117
##    260        2.7560             nan     0.0500   -0.0069
##    280        2.6777             nan     0.0500   -0.0183
##    300        2.6141             nan     0.0500   -0.0129
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.2638             nan     0.0500    4.9113
##      2       51.4327             nan     0.0500    5.1917
##      3       47.1931             nan     0.0500    4.5336
##      4       43.3367             nan     0.0500    3.5749
##      5       39.7113             nan     0.0500    3.3623
##      6       36.5267             nan     0.0500    3.0600
##      7       33.6145             nan     0.0500    2.9483
##      8       30.9439             nan     0.0500    2.4926
##      9       28.6520             nan     0.0500    2.3665
##     10       26.3901             nan     0.0500    2.0501
##     20       12.7373             nan     0.0500    0.6948
##     40        5.1322             nan     0.0500    0.1296
##     60        3.3519             nan     0.0500   -0.0029
##     80        2.7871             nan     0.0500   -0.0279
##    100        2.4228             nan     0.0500   -0.0154
##    120        2.1971             nan     0.0500   -0.0238
##    140        1.9685             nan     0.0500   -0.0051
##    160        1.8083             nan     0.0500   -0.0032
##    180        1.6591             nan     0.0500   -0.0111
##    200        1.5165             nan     0.0500   -0.0174
##    220        1.3881             nan     0.0500   -0.0046
##    240        1.2850             nan     0.0500   -0.0086
##    260        1.1870             nan     0.0500   -0.0076
##    280        1.0969             nan     0.0500   -0.0124
##    300        1.0232             nan     0.0500   -0.0125
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.3257             nan     0.0500    4.9082
##      2       51.7223             nan     0.0500    4.1841
##      3       47.2697             nan     0.0500    4.0280
##      4       43.3561             nan     0.0500    3.7546
##      5       39.8132             nan     0.0500    3.4950
##      6       36.6496             nan     0.0500    3.0146
##      7       33.7619             nan     0.0500    2.8775
##      8       31.0063             nan     0.0500    2.6138
##      9       28.8234             nan     0.0500    2.1678
##     10       26.6081             nan     0.0500    2.1545
##     20       13.1800             nan     0.0500    0.7379
##     40        5.3308             nan     0.0500    0.1217
##     60        3.7864             nan     0.0500   -0.0058
##     80        3.2331             nan     0.0500   -0.0191
##    100        2.9085             nan     0.0500   -0.0242
##    120        2.6610             nan     0.0500   -0.0175
##    140        2.4777             nan     0.0500   -0.0077
##    160        2.3397             nan     0.0500   -0.0256
##    180        2.2029             nan     0.0500   -0.0218
##    200        2.0719             nan     0.0500   -0.0258
##    220        1.9302             nan     0.0500   -0.0066
##    240        1.8126             nan     0.0500   -0.0084
##    260        1.7167             nan     0.0500   -0.0163
##    280        1.6456             nan     0.0500   -0.0303
##    300        1.5770             nan     0.0500   -0.0154
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.6400             nan     0.0500    4.3550
##      2       52.0289             nan     0.0500    4.9811
##      3       47.9229             nan     0.0500    3.8790
##      4       44.0696             nan     0.0500    4.2387
##      5       40.5396             nan     0.0500    3.5277
##      6       37.4770             nan     0.0500    2.9603
##      7       34.4191             nan     0.0500    2.9682
##      8       31.6216             nan     0.0500    2.6704
##      9       29.2256             nan     0.0500    2.2461
##     10       27.0938             nan     0.0500    2.3321
##     20       13.3959             nan     0.0500    0.6797
##     40        5.6904             nan     0.0500    0.1482
##     60        4.1340             nan     0.0500    0.0103
##     80        3.6248             nan     0.0500   -0.0299
##    100        3.3372             nan     0.0500   -0.0193
##    120        3.0990             nan     0.0500   -0.0128
##    140        2.9222             nan     0.0500   -0.0148
##    160        2.7660             nan     0.0500   -0.0206
##    180        2.6155             nan     0.0500   -0.0071
##    200        2.4911             nan     0.0500   -0.0093
##    220        2.3656             nan     0.0500   -0.0193
##    240        2.2562             nan     0.0500   -0.0124
##    260        2.1599             nan     0.0500   -0.0316
##    280        2.0814             nan     0.0500   -0.0160
##    300        1.9923             nan     0.0500   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.2847             nan     0.1000    7.5454
##      2       48.6925             nan     0.1000    5.3311
##      3       44.1672             nan     0.1000    5.0422
##      4       39.6491             nan     0.1000    4.4760
##      5       35.7243             nan     0.1000    3.6096
##      6       32.6440             nan     0.1000    2.8981
##      7       29.4232             nan     0.1000    2.9324
##      8       27.0610             nan     0.1000    2.1609
##      9       24.9332             nan     0.1000    1.9953
##     10       22.8532             nan     0.1000    1.8774
##     20       11.9913             nan     0.1000    0.5764
##     40        5.9687             nan     0.1000    0.0648
##     60        4.6823             nan     0.1000    0.0043
##     80        4.3640             nan     0.1000   -0.0267
##    100        4.1831             nan     0.1000   -0.0480
##    120        4.0730             nan     0.1000   -0.0253
##    140        3.9445             nan     0.1000   -0.0369
##    160        3.8502             nan     0.1000    0.0005
##    180        3.7419             nan     0.1000   -0.0084
##    200        3.6585             nan     0.1000   -0.0078
##    220        3.5719             nan     0.1000   -0.0146
##    240        3.4860             nan     0.1000   -0.0248
##    260        3.4062             nan     0.1000   -0.0156
##    280        3.3333             nan     0.1000   -0.0101
##    300        3.2440             nan     0.1000   -0.0143
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.4360             nan     0.1000    7.1786
##      2       48.2436             nan     0.1000    5.9560
##      3       42.9664             nan     0.1000    4.7632
##      4       38.5017             nan     0.1000    3.8203
##      5       34.9034             nan     0.1000    3.6767
##      6       31.8816             nan     0.1000    3.1260
##      7       28.7641             nan     0.1000    2.6304
##      8       26.3670             nan     0.1000    2.1303
##      9       24.0570             nan     0.1000    1.9654
##     10       21.9052             nan     0.1000    1.5430
##     20       11.6009             nan     0.1000    0.4593
##     40        6.0548             nan     0.1000    0.0773
##     60        4.7745             nan     0.1000   -0.0061
##     80        4.4454             nan     0.1000   -0.0107
##    100        4.2585             nan     0.1000   -0.0184
##    120        4.1151             nan     0.1000   -0.0049
##    140        4.0180             nan     0.1000   -0.0325
##    160        3.9088             nan     0.1000   -0.0065
##    180        3.8272             nan     0.1000   -0.0172
##    200        3.7435             nan     0.1000   -0.0121
##    220        3.6725             nan     0.1000   -0.0367
##    240        3.5888             nan     0.1000   -0.0115
##    260        3.5253             nan     0.1000   -0.0218
##    280        3.4719             nan     0.1000   -0.0266
##    300        3.4208             nan     0.1000   -0.0139
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.9771             nan     0.1000    7.0895
##      2       47.9560             nan     0.1000    5.8764
##      3       43.0336             nan     0.1000    4.8825
##      4       38.8824             nan     0.1000    3.9261
##      5       35.1650             nan     0.1000    3.3880
##      6       31.7645             nan     0.1000    3.4813
##      7       28.9339             nan     0.1000    2.4061
##      8       26.3186             nan     0.1000    2.1991
##      9       24.0556             nan     0.1000    1.9092
##     10       22.2098             nan     0.1000    1.9348
##     20       12.0798             nan     0.1000    0.5723
##     40        6.2028             nan     0.1000    0.1175
##     60        5.0612             nan     0.1000    0.0113
##     80        4.7066             nan     0.1000   -0.0083
##    100        4.5007             nan     0.1000   -0.0166
##    120        4.3527             nan     0.1000   -0.0188
##    140        4.2346             nan     0.1000   -0.0182
##    160        4.1149             nan     0.1000   -0.0065
##    180        4.0134             nan     0.1000   -0.0158
##    200        3.9291             nan     0.1000   -0.0067
##    220        3.8515             nan     0.1000   -0.0139
##    240        3.7660             nan     0.1000   -0.0163
##    260        3.7190             nan     0.1000   -0.0038
##    280        3.6664             nan     0.1000   -0.0144
##    300        3.5887             nan     0.1000   -0.0125
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.7831             nan     0.1000    9.1923
##      2       43.9110             nan     0.1000    8.8358
##      3       37.2493             nan     0.1000    6.8920
##      4       31.9451             nan     0.1000    5.2992
##      5       28.0059             nan     0.1000    3.8268
##      6       24.2273             nan     0.1000    3.6710
##      7       21.1069             nan     0.1000    2.6253
##      8       18.8089             nan     0.1000    2.3356
##      9       16.6477             nan     0.1000    2.1193
##     10       15.1227             nan     0.1000    1.4087
##     20        6.4822             nan     0.1000    0.2781
##     40        3.6473             nan     0.1000    0.0004
##     60        3.0684             nan     0.1000   -0.0373
##     80        2.6830             nan     0.1000   -0.0257
##    100        2.3901             nan     0.1000   -0.0758
##    120        2.1295             nan     0.1000   -0.0589
##    140        1.9358             nan     0.1000   -0.0120
##    160        1.7685             nan     0.1000   -0.0249
##    180        1.5799             nan     0.1000   -0.0172
##    200        1.4666             nan     0.1000   -0.0247
##    220        1.3486             nan     0.1000   -0.0160
##    240        1.2328             nan     0.1000   -0.0135
##    260        1.1368             nan     0.1000   -0.0163
##    280        1.0465             nan     0.1000   -0.0074
##    300        0.9787             nan     0.1000   -0.0133
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.4299             nan     0.1000    9.3723
##      2       44.2958             nan     0.1000    8.8230
##      3       37.6021             nan     0.1000    6.3263
##      4       32.4734             nan     0.1000    5.4821
##      5       27.9151             nan     0.1000    4.2789
##      6       24.1962             nan     0.1000    3.4308
##      7       21.0915             nan     0.1000    3.0697
##      8       18.3793             nan     0.1000    2.0839
##      9       16.3224             nan     0.1000    1.7534
##     10       14.6533             nan     0.1000    1.7295
##     20        6.3795             nan     0.1000    0.2951
##     40        3.7911             nan     0.1000   -0.0214
##     60        3.2749             nan     0.1000   -0.0337
##     80        2.9521             nan     0.1000   -0.0505
##    100        2.6855             nan     0.1000   -0.0448
##    120        2.4726             nan     0.1000   -0.0195
##    140        2.3036             nan     0.1000   -0.0284
##    160        2.1852             nan     0.1000   -0.0343
##    180        2.0308             nan     0.1000   -0.0130
##    200        1.9137             nan     0.1000   -0.0482
##    220        1.7811             nan     0.1000   -0.0243
##    240        1.7000             nan     0.1000   -0.0366
##    260        1.6306             nan     0.1000   -0.0191
##    280        1.5485             nan     0.1000   -0.0163
##    300        1.4767             nan     0.1000   -0.0282
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.6957             nan     0.1000   10.2754
##      2       43.9888             nan     0.1000    5.8718
##      3       37.2893             nan     0.1000    6.3956
##      4       31.7999             nan     0.1000    5.0527
##      5       27.3631             nan     0.1000    4.6902
##      6       23.6505             nan     0.1000    3.0164
##      7       20.5370             nan     0.1000    2.7276
##      8       17.9467             nan     0.1000    2.4883
##      9       16.2444             nan     0.1000    1.7189
##     10       14.4171             nan     0.1000    1.7024
##     20        6.3703             nan     0.1000    0.2859
##     40        4.0622             nan     0.1000   -0.0069
##     60        3.4892             nan     0.1000   -0.0649
##     80        3.1642             nan     0.1000   -0.0277
##    100        2.9496             nan     0.1000   -0.0575
##    120        2.7454             nan     0.1000   -0.0347
##    140        2.6094             nan     0.1000   -0.0205
##    160        2.4530             nan     0.1000   -0.0320
##    180        2.3304             nan     0.1000   -0.0473
##    200        2.1898             nan     0.1000   -0.0050
##    220        2.0959             nan     0.1000   -0.0191
##    240        1.9861             nan     0.1000   -0.0119
##    260        1.8927             nan     0.1000   -0.0231
##    280        1.8116             nan     0.1000   -0.0159
##    300        1.7470             nan     0.1000   -0.0239
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.5094             nan     0.1000   10.0884
##      2       42.6263             nan     0.1000    7.9548
##      3       36.0184             nan     0.1000    7.0857
##      4       30.6992             nan     0.1000    5.2137
##      5       26.1656             nan     0.1000    4.0115
##      6       22.3809             nan     0.1000    3.1315
##      7       19.2653             nan     0.1000    2.8562
##      8       16.5477             nan     0.1000    2.4638
##      9       14.4104             nan     0.1000    1.9341
##     10       12.7724             nan     0.1000    1.4613
##     20        4.9430             nan     0.1000    0.3141
##     40        2.8922             nan     0.1000   -0.0826
##     60        2.2484             nan     0.1000   -0.0216
##     80        1.8591             nan     0.1000   -0.0362
##    100        1.5492             nan     0.1000   -0.0420
##    120        1.3282             nan     0.1000   -0.0318
##    140        1.1419             nan     0.1000   -0.0140
##    160        0.9678             nan     0.1000   -0.0162
##    180        0.8545             nan     0.1000   -0.0158
##    200        0.7652             nan     0.1000   -0.0185
##    220        0.6638             nan     0.1000   -0.0193
##    240        0.5770             nan     0.1000   -0.0133
##    260        0.5156             nan     0.1000   -0.0055
##    280        0.4601             nan     0.1000   -0.0049
##    300        0.4192             nan     0.1000   -0.0075
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.5295             nan     0.1000   10.7635
##      2       42.9585             nan     0.1000    8.2234
##      3       36.4052             nan     0.1000    6.6474
##      4       31.1299             nan     0.1000    4.5835
##      5       26.2568             nan     0.1000    4.3051
##      6       22.3920             nan     0.1000    3.5802
##      7       19.3733             nan     0.1000    2.9887
##      8       16.7005             nan     0.1000    2.3146
##      9       14.6994             nan     0.1000    1.9832
##     10       13.0350             nan     0.1000    1.5186
##     20        5.2289             nan     0.1000    0.1753
##     40        3.0827             nan     0.1000   -0.0080
##     60        2.6379             nan     0.1000   -0.0207
##     80        2.2997             nan     0.1000   -0.0350
##    100        2.0528             nan     0.1000   -0.0432
##    120        1.8291             nan     0.1000   -0.0384
##    140        1.6362             nan     0.1000   -0.0106
##    160        1.4719             nan     0.1000   -0.0286
##    180        1.3424             nan     0.1000   -0.0254
##    200        1.2191             nan     0.1000   -0.0260
##    220        1.1263             nan     0.1000   -0.0248
##    240        1.0218             nan     0.1000   -0.0117
##    260        0.9483             nan     0.1000   -0.0149
##    280        0.8664             nan     0.1000   -0.0131
##    300        0.8018             nan     0.1000   -0.0111
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.5351             nan     0.1000    9.6900
##      2       43.1175             nan     0.1000    8.7664
##      3       36.3583             nan     0.1000    6.5872
##      4       30.3095             nan     0.1000    5.8680
##      5       25.5715             nan     0.1000    4.2419
##      6       22.0715             nan     0.1000    3.6646
##      7       19.0115             nan     0.1000    3.2653
##      8       16.6682             nan     0.1000    2.4997
##      9       14.6179             nan     0.1000    1.9630
##     10       12.9433             nan     0.1000    1.5236
##     20        5.6848             nan     0.1000    0.2340
##     40        3.7360             nan     0.1000   -0.0338
##     60        3.2756             nan     0.1000   -0.0596
##     80        2.9915             nan     0.1000   -0.0369
##    100        2.7077             nan     0.1000   -0.0549
##    120        2.4969             nan     0.1000   -0.0376
##    140        2.3198             nan     0.1000   -0.0333
##    160        2.1127             nan     0.1000   -0.0418
##    180        1.9531             nan     0.1000   -0.0190
##    200        1.8175             nan     0.1000   -0.0179
##    220        1.6914             nan     0.1000   -0.0225
##    240        1.5730             nan     0.1000   -0.0135
##    260        1.4840             nan     0.1000   -0.0196
##    280        1.3939             nan     0.1000   -0.0142
##    300        1.3282             nan     0.1000   -0.0197
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.2782             nan     0.0100    0.7547
##      2       60.4911             nan     0.0100    0.7609
##      3       59.8464             nan     0.0100    0.6957
##      4       59.1555             nan     0.0100    0.7994
##      5       58.4687             nan     0.0100    0.7077
##      6       57.7765             nan     0.0100    0.6926
##      7       57.0277             nan     0.0100    0.6271
##      8       56.3670             nan     0.0100    0.6321
##      9       55.6581             nan     0.0100    0.6682
##     10       55.0170             nan     0.0100    0.6144
##     20       49.3185             nan     0.0100    0.5477
##     40       39.8234             nan     0.0100    0.3762
##     60       32.7438             nan     0.0100    0.3144
##     80       27.4280             nan     0.0100    0.2301
##    100       23.1311             nan     0.0100    0.1549
##    120       19.8965             nan     0.0100    0.1442
##    140       17.3439             nan     0.0100    0.0960
##    160       15.3253             nan     0.0100    0.0917
##    180       13.5949             nan     0.0100    0.0590
##    200       12.2595             nan     0.0100    0.0276
##    220       11.0968             nan     0.0100    0.0404
##    240       10.1079             nan     0.0100    0.0339
##    260        9.2429             nan     0.0100    0.0225
##    280        8.5532             nan     0.0100    0.0331
##    300        7.9692             nan     0.0100    0.0274
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.2032             nan     0.0100    0.7791
##      2       60.4498             nan     0.0100    0.7671
##      3       59.7171             nan     0.0100    0.6749
##      4       59.1124             nan     0.0100    0.6010
##      5       58.3623             nan     0.0100    0.6742
##      6       57.6567             nan     0.0100    0.6628
##      7       56.9899             nan     0.0100    0.7006
##      8       56.2986             nan     0.0100    0.6062
##      9       55.5947             nan     0.0100    0.6568
##     10       54.9510             nan     0.0100    0.6569
##     20       49.0352             nan     0.0100    0.5077
##     40       39.5332             nan     0.0100    0.4050
##     60       32.5387             nan     0.0100    0.3055
##     80       27.1126             nan     0.0100    0.1973
##    100       23.0153             nan     0.0100    0.1716
##    120       19.8695             nan     0.0100    0.1226
##    140       17.2766             nan     0.0100    0.1083
##    160       15.2465             nan     0.0100    0.0742
##    180       13.5743             nan     0.0100    0.0529
##    200       12.1343             nan     0.0100    0.0602
##    220       10.9952             nan     0.0100    0.0422
##    240       10.0011             nan     0.0100    0.0347
##    260        9.1952             nan     0.0100    0.0314
##    280        8.4795             nan     0.0100    0.0161
##    300        7.8864             nan     0.0100    0.0070
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.1751             nan     0.0100    0.7565
##      2       60.4646             nan     0.0100    0.7551
##      3       59.7129             nan     0.0100    0.7414
##      4       58.9753             nan     0.0100    0.6788
##      5       58.2159             nan     0.0100    0.6441
##      6       57.4814             nan     0.0100    0.6932
##      7       56.7929             nan     0.0100    0.6312
##      8       56.0925             nan     0.0100    0.6759
##      9       55.4329             nan     0.0100    0.6614
##     10       54.7291             nan     0.0100    0.6077
##     20       48.7355             nan     0.0100    0.5156
##     40       39.4375             nan     0.0100    0.3859
##     60       32.4370             nan     0.0100    0.2478
##     80       27.0441             nan     0.0100    0.2366
##    100       22.8406             nan     0.0100    0.1715
##    120       19.6887             nan     0.0100    0.1411
##    140       17.1510             nan     0.0100    0.0925
##    160       15.1039             nan     0.0100    0.0590
##    180       13.4811             nan     0.0100    0.0641
##    200       12.1224             nan     0.0100    0.0511
##    220       10.9726             nan     0.0100    0.0379
##    240       10.0197             nan     0.0100    0.0383
##    260        9.2190             nan     0.0100    0.0167
##    280        8.5204             nan     0.0100    0.0263
##    300        7.9473             nan     0.0100    0.0253
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.9673             nan     0.0100    1.1014
##      2       60.0113             nan     0.0100    1.0332
##      3       59.0835             nan     0.0100    0.9142
##      4       58.1361             nan     0.0100    0.9753
##      5       57.2506             nan     0.0100    0.9082
##      6       56.2395             nan     0.0100    0.8998
##      7       55.2943             nan     0.0100    0.8753
##      8       54.3674             nan     0.0100    0.8091
##      9       53.4745             nan     0.0100    0.8212
##     10       52.6147             nan     0.0100    0.8496
##     20       44.9554             nan     0.0100    0.7143
##     40       33.1898             nan     0.0100    0.4872
##     60       25.0167             nan     0.0100    0.3252
##     80       19.1779             nan     0.0100    0.2385
##    100       15.0999             nan     0.0100    0.1559
##    120       12.2186             nan     0.0100    0.1355
##    140       10.0133             nan     0.0100    0.0732
##    160        8.4532             nan     0.0100    0.0629
##    180        7.2764             nan     0.0100    0.0459
##    200        6.4265             nan     0.0100    0.0390
##    220        5.7298             nan     0.0100    0.0152
##    240        5.1990             nan     0.0100    0.0128
##    260        4.8165             nan     0.0100    0.0051
##    280        4.5014             nan     0.0100    0.0048
##    300        4.2759             nan     0.0100    0.0027
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.9618             nan     0.0100    0.9912
##      2       59.9884             nan     0.0100    0.9463
##      3       58.9741             nan     0.0100    1.0465
##      4       57.9778             nan     0.0100    0.9638
##      5       57.0541             nan     0.0100    0.9541
##      6       56.1177             nan     0.0100    0.9155
##      7       55.1801             nan     0.0100    0.9345
##      8       54.3021             nan     0.0100    0.8906
##      9       53.4836             nan     0.0100    0.8987
##     10       52.6431             nan     0.0100    0.8870
##     20       44.8019             nan     0.0100    0.7496
##     40       33.1322             nan     0.0100    0.4327
##     60       24.8941             nan     0.0100    0.3239
##     80       19.2709             nan     0.0100    0.2609
##    100       15.1453             nan     0.0100    0.1614
##    120       12.2285             nan     0.0100    0.0973
##    140       10.1777             nan     0.0100    0.0776
##    160        8.5915             nan     0.0100    0.0510
##    180        7.4301             nan     0.0100    0.0347
##    200        6.5481             nan     0.0100    0.0367
##    220        5.8612             nan     0.0100    0.0246
##    240        5.3379             nan     0.0100    0.0139
##    260        4.9536             nan     0.0100    0.0146
##    280        4.6432             nan     0.0100    0.0056
##    300        4.4226             nan     0.0100    0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.9484             nan     0.0100    0.9642
##      2       59.9405             nan     0.0100    1.1000
##      3       58.9755             nan     0.0100    0.9454
##      4       58.0619             nan     0.0100    0.9695
##      5       57.1092             nan     0.0100    0.9359
##      6       56.1662             nan     0.0100    0.7976
##      7       55.2474             nan     0.0100    0.8715
##      8       54.3634             nan     0.0100    0.7532
##      9       53.5102             nan     0.0100    0.9008
##     10       52.6693             nan     0.0100    0.8708
##     20       44.8179             nan     0.0100    0.6580
##     40       33.1220             nan     0.0100    0.4176
##     60       24.9934             nan     0.0100    0.3294
##     80       19.2953             nan     0.0100    0.2322
##    100       15.2673             nan     0.0100    0.1688
##    120       12.3992             nan     0.0100    0.1088
##    140       10.2690             nan     0.0100    0.0764
##    160        8.7538             nan     0.0100    0.0511
##    180        7.5496             nan     0.0100    0.0419
##    200        6.6881             nan     0.0100    0.0216
##    220        6.0339             nan     0.0100    0.0204
##    240        5.5379             nan     0.0100    0.0164
##    260        5.1856             nan     0.0100    0.0136
##    280        4.8905             nan     0.0100    0.0114
##    300        4.6574             nan     0.0100    0.0027
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.9066             nan     0.0100    1.0793
##      2       59.8637             nan     0.0100    1.1211
##      3       58.7984             nan     0.0100    1.0466
##      4       57.7997             nan     0.0100    0.9490
##      5       56.8108             nan     0.0100    0.8728
##      6       55.8808             nan     0.0100    0.9942
##      7       54.9091             nan     0.0100    0.8586
##      8       53.9576             nan     0.0100    0.9741
##      9       52.9840             nan     0.0100    0.9174
##     10       52.0550             nan     0.0100    0.9862
##     20       43.9723             nan     0.0100    0.7460
##     40       31.6908             nan     0.0100    0.4533
##     60       23.2891             nan     0.0100    0.3550
##     80       17.4293             nan     0.0100    0.2488
##    100       13.2933             nan     0.0100    0.1942
##    120       10.4793             nan     0.0100    0.1200
##    140        8.5063             nan     0.0100    0.0668
##    160        7.0616             nan     0.0100    0.0342
##    180        6.0231             nan     0.0100    0.0283
##    200        5.2383             nan     0.0100    0.0169
##    220        4.6363             nan     0.0100    0.0148
##    240        4.2156             nan     0.0100    0.0078
##    260        3.8933             nan     0.0100    0.0077
##    280        3.6321             nan     0.0100    0.0050
##    300        3.4228             nan     0.0100    0.0033
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.8542             nan     0.0100    1.0554
##      2       59.7785             nan     0.0100    0.9839
##      3       58.7497             nan     0.0100    0.9876
##      4       57.7135             nan     0.0100    1.1079
##      5       56.6976             nan     0.0100    0.9991
##      6       55.6967             nan     0.0100    0.9325
##      7       54.7520             nan     0.0100    0.9363
##      8       53.8409             nan     0.0100    0.9579
##      9       52.9597             nan     0.0100    0.9174
##     10       52.0546             nan     0.0100    0.8634
##     20       44.0498             nan     0.0100    0.7668
##     40       31.6326             nan     0.0100    0.4931
##     60       23.2712             nan     0.0100    0.3170
##     80       17.4886             nan     0.0100    0.2272
##    100       13.4487             nan     0.0100    0.1517
##    120       10.5255             nan     0.0100    0.1185
##    140        8.5247             nan     0.0100    0.0850
##    160        7.0966             nan     0.0100    0.0504
##    180        6.0861             nan     0.0100    0.0232
##    200        5.3776             nan     0.0100    0.0210
##    220        4.8300             nan     0.0100    0.0014
##    240        4.4625             nan     0.0100    0.0132
##    260        4.1338             nan     0.0100    0.0102
##    280        3.9148             nan     0.0100    0.0011
##    300        3.7296             nan     0.0100    0.0021
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.8955             nan     0.0100    1.0620
##      2       59.8036             nan     0.0100    0.9926
##      3       58.8117             nan     0.0100    1.1072
##      4       57.8520             nan     0.0100    0.9819
##      5       56.8469             nan     0.0100    0.8601
##      6       55.8695             nan     0.0100    1.0273
##      7       54.9189             nan     0.0100    0.8766
##      8       54.0025             nan     0.0100    0.9191
##      9       53.1187             nan     0.0100    0.8876
##     10       52.1809             nan     0.0100    0.9734
##     20       44.2258             nan     0.0100    0.7035
##     40       31.9399             nan     0.0100    0.4012
##     60       23.5770             nan     0.0100    0.3014
##     80       17.6580             nan     0.0100    0.2422
##    100       13.6456             nan     0.0100    0.1330
##    120       10.8694             nan     0.0100    0.1122
##    140        8.8884             nan     0.0100    0.0833
##    160        7.5281             nan     0.0100    0.0528
##    180        6.5305             nan     0.0100    0.0355
##    200        5.8123             nan     0.0100    0.0167
##    220        5.2650             nan     0.0100    0.0144
##    240        4.8759             nan     0.0100    0.0103
##    260        4.5771             nan     0.0100    0.0071
##    280        4.3504             nan     0.0100    0.0051
##    300        4.1737             nan     0.0100    0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.2006             nan     0.0500    3.7901
##      2       55.2052             nan     0.0500    3.2358
##      3       51.9308             nan     0.0500    3.1021
##      4       48.9960             nan     0.0500    2.8302
##      5       46.3911             nan     0.0500    2.6234
##      6       43.8995             nan     0.0500    2.2379
##      7       41.5016             nan     0.0500    2.1831
##      8       39.6352             nan     0.0500    1.9675
##      9       37.7607             nan     0.0500    2.0399
##     10       36.0613             nan     0.0500    1.7732
##     20       23.1878             nan     0.0500    0.9456
##     40       11.8278             nan     0.0500    0.2510
##     60        7.7737             nan     0.0500    0.1266
##     80        5.9591             nan     0.0500    0.0387
##    100        5.0405             nan     0.0500    0.0181
##    120        4.5846             nan     0.0500   -0.0046
##    140        4.3993             nan     0.0500   -0.0107
##    160        4.2509             nan     0.0500   -0.0193
##    180        4.1496             nan     0.0500    0.0017
##    200        4.0758             nan     0.0500   -0.0348
##    220        3.9887             nan     0.0500   -0.0008
##    240        3.9232             nan     0.0500   -0.0018
##    260        3.8710             nan     0.0500   -0.0108
##    280        3.8077             nan     0.0500   -0.0006
##    300        3.7601             nan     0.0500   -0.0061
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.3583             nan     0.0500    3.7702
##      2       54.6627             nan     0.0500    3.3922
##      3       51.5871             nan     0.0500    2.9825
##      4       48.6161             nan     0.0500    2.8540
##      5       45.7592             nan     0.0500    2.4412
##      6       43.2429             nan     0.0500    2.3682
##      7       40.8529             nan     0.0500    2.1225
##      8       38.8422             nan     0.0500    1.9111
##      9       36.8274             nan     0.0500    1.6896
##     10       35.1189             nan     0.0500    1.6276
##     20       22.6289             nan     0.0500    0.8623
##     40       12.0454             nan     0.0500    0.3335
##     60        8.0545             nan     0.0500    0.0532
##     80        6.0855             nan     0.0500    0.0602
##    100        5.1732             nan     0.0500    0.0125
##    120        4.7647             nan     0.0500    0.0066
##    140        4.5707             nan     0.0500   -0.0092
##    160        4.4135             nan     0.0500   -0.0063
##    180        4.2780             nan     0.0500   -0.0085
##    200        4.1921             nan     0.0500   -0.0186
##    220        4.1194             nan     0.0500    0.0002
##    240        4.0499             nan     0.0500   -0.0172
##    260        4.0078             nan     0.0500   -0.0100
##    280        3.9571             nan     0.0500   -0.0178
##    300        3.9042             nan     0.0500   -0.0222
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.1475             nan     0.0500    3.4288
##      2       54.9305             nan     0.0500    3.3203
##      3       51.6950             nan     0.0500    3.1132
##      4       48.5420             nan     0.0500    2.7732
##      5       45.8491             nan     0.0500    2.4686
##      6       43.7292             nan     0.0500    1.9523
##      7       41.5442             nan     0.0500    2.3096
##      8       39.2984             nan     0.0500    2.0395
##      9       37.3538             nan     0.0500    1.8446
##     10       35.5245             nan     0.0500    1.6948
##     20       22.5341             nan     0.0500    0.8060
##     40       11.8316             nan     0.0500    0.2360
##     60        7.7154             nan     0.0500    0.1049
##     80        6.0048             nan     0.0500    0.0531
##    100        5.2107             nan     0.0500   -0.0017
##    120        4.8325             nan     0.0500   -0.0099
##    140        4.6091             nan     0.0500   -0.0144
##    160        4.4715             nan     0.0500   -0.0115
##    180        4.3627             nan     0.0500   -0.0154
##    200        4.2785             nan     0.0500   -0.0046
##    220        4.1841             nan     0.0500   -0.0057
##    240        4.1275             nan     0.0500   -0.0297
##    260        4.0548             nan     0.0500   -0.0211
##    280        3.9984             nan     0.0500   -0.0098
##    300        3.9480             nan     0.0500   -0.0178
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.8468             nan     0.0500    4.9823
##      2       52.2897             nan     0.0500    4.2509
##      3       48.2493             nan     0.0500    3.8500
##      4       44.2044             nan     0.0500    3.9502
##      5       40.9998             nan     0.0500    3.1167
##      6       37.9338             nan     0.0500    3.5273
##      7       35.0813             nan     0.0500    2.7784
##      8       32.5656             nan     0.0500    2.6083
##      9       30.2512             nan     0.0500    2.3750
##     10       28.1495             nan     0.0500    1.8037
##     20       14.6402             nan     0.0500    0.7339
##     40        6.2141             nan     0.0500    0.2007
##     60        4.1856             nan     0.0500    0.0297
##     80        3.5389             nan     0.0500   -0.0063
##    100        3.2361             nan     0.0500   -0.0130
##    120        2.9660             nan     0.0500   -0.0140
##    140        2.7125             nan     0.0500   -0.0170
##    160        2.5298             nan     0.0500   -0.0096
##    180        2.3619             nan     0.0500   -0.0103
##    200        2.2383             nan     0.0500   -0.0148
##    220        2.1349             nan     0.0500   -0.0065
##    240        2.0271             nan     0.0500   -0.0095
##    260        1.9195             nan     0.0500   -0.0094
##    280        1.8284             nan     0.0500   -0.0074
##    300        1.7672             nan     0.0500   -0.0156
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.9699             nan     0.0500    4.8253
##      2       52.2343             nan     0.0500    4.3562
##      3       48.2032             nan     0.0500    3.6253
##      4       44.6032             nan     0.0500    3.7686
##      5       41.6168             nan     0.0500    3.4270
##      6       38.4945             nan     0.0500    3.2866
##      7       35.6761             nan     0.0500    2.6693
##      8       33.0318             nan     0.0500    2.6381
##      9       30.6494             nan     0.0500    2.4535
##     10       28.4127             nan     0.0500    2.2697
##     20       14.8107             nan     0.0500    0.8712
##     40        6.3471             nan     0.0500    0.1360
##     60        4.3693             nan     0.0500    0.0035
##     80        3.7617             nan     0.0500    0.0149
##    100        3.4241             nan     0.0500   -0.0335
##    120        3.2004             nan     0.0500   -0.0164
##    140        3.0228             nan     0.0500   -0.0222
##    160        2.8736             nan     0.0500   -0.0215
##    180        2.7380             nan     0.0500   -0.0256
##    200        2.6255             nan     0.0500   -0.0063
##    220        2.5174             nan     0.0500   -0.0053
##    240        2.4024             nan     0.0500   -0.0222
##    260        2.3129             nan     0.0500   -0.0090
##    280        2.2234             nan     0.0500   -0.0149
##    300        2.1569             nan     0.0500   -0.0081
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.0318             nan     0.0500    5.2241
##      2       52.3588             nan     0.0500    4.3338
##      3       48.2314             nan     0.0500    3.8491
##      4       44.6297             nan     0.0500    3.8542
##      5       41.2513             nan     0.0500    3.3895
##      6       37.9487             nan     0.0500    2.9241
##      7       35.1918             nan     0.0500    2.8625
##      8       32.4621             nan     0.0500    2.2926
##      9       30.1004             nan     0.0500    2.1692
##     10       28.0571             nan     0.0500    2.1696
##     20       14.7817             nan     0.0500    0.8538
##     40        6.6302             nan     0.0500    0.1822
##     60        4.6501             nan     0.0500    0.0040
##     80        4.0142             nan     0.0500    0.0014
##    100        3.7017             nan     0.0500   -0.0054
##    120        3.4814             nan     0.0500   -0.0426
##    140        3.2993             nan     0.0500   -0.0059
##    160        3.1524             nan     0.0500   -0.0381
##    180        3.0405             nan     0.0500   -0.0204
##    200        2.9298             nan     0.0500   -0.0072
##    220        2.8203             nan     0.0500   -0.0190
##    240        2.7347             nan     0.0500   -0.0033
##    260        2.6470             nan     0.0500   -0.0074
##    280        2.5829             nan     0.0500   -0.0202
##    300        2.4910             nan     0.0500   -0.0143
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.4199             nan     0.0500    6.1120
##      2       51.5760             nan     0.0500    4.7250
##      3       47.2701             nan     0.0500    4.0343
##      4       43.4141             nan     0.0500    3.8000
##      5       39.8707             nan     0.0500    3.5911
##      6       36.6415             nan     0.0500    3.0290
##      7       33.9256             nan     0.0500    2.8129
##      8       31.2809             nan     0.0500    2.3267
##      9       28.8015             nan     0.0500    2.0608
##     10       26.4720             nan     0.0500    1.8536
##     20       12.7148             nan     0.0500    0.8396
##     40        5.1527             nan     0.0500    0.1045
##     60        3.5056             nan     0.0500    0.0233
##     80        2.9058             nan     0.0500    0.0064
##    100        2.5195             nan     0.0500   -0.0095
##    120        2.2780             nan     0.0500   -0.0139
##    140        2.0464             nan     0.0500   -0.0225
##    160        1.8618             nan     0.0500   -0.0086
##    180        1.6955             nan     0.0500   -0.0071
##    200        1.5669             nan     0.0500   -0.0114
##    220        1.4465             nan     0.0500   -0.0047
##    240        1.3395             nan     0.0500   -0.0188
##    260        1.2408             nan     0.0500   -0.0101
##    280        1.1445             nan     0.0500   -0.0082
##    300        1.0658             nan     0.0500   -0.0069
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.7837             nan     0.0500    4.9749
##      2       51.9329             nan     0.0500    4.7119
##      3       47.7165             nan     0.0500    4.2591
##      4       43.7532             nan     0.0500    3.4393
##      5       40.3199             nan     0.0500    3.7191
##      6       37.1990             nan     0.0500    3.0032
##      7       34.1873             nan     0.0500    2.4679
##      8       31.5847             nan     0.0500    2.8507
##      9       29.1043             nan     0.0500    2.2560
##     10       26.8693             nan     0.0500    1.9285
##     20       13.4486             nan     0.0500    0.7077
##     40        5.4059             nan     0.0500    0.1273
##     60        3.7388             nan     0.0500    0.0116
##     80        3.1674             nan     0.0500   -0.0052
##    100        2.8128             nan     0.0500   -0.0083
##    120        2.5731             nan     0.0500   -0.0296
##    140        2.3753             nan     0.0500   -0.0186
##    160        2.2237             nan     0.0500   -0.0123
##    180        2.0932             nan     0.0500   -0.0134
##    200        1.9533             nan     0.0500   -0.0101
##    220        1.8481             nan     0.0500   -0.0152
##    240        1.7331             nan     0.0500   -0.0098
##    260        1.6449             nan     0.0500   -0.0103
##    280        1.5608             nan     0.0500   -0.0136
##    300        1.4904             nan     0.0500   -0.0185
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.8951             nan     0.0500    4.6450
##      2       52.0892             nan     0.0500    4.7157
##      3       47.7050             nan     0.0500    4.1144
##      4       43.7895             nan     0.0500    3.8474
##      5       40.3450             nan     0.0500    3.4873
##      6       37.1714             nan     0.0500    2.8507
##      7       34.1761             nan     0.0500    2.7566
##      8       31.4909             nan     0.0500    2.6038
##      9       29.0280             nan     0.0500    2.3011
##     10       26.8480             nan     0.0500    1.7609
##     20       12.9348             nan     0.0500    0.8350
##     40        5.5096             nan     0.0500    0.1303
##     60        4.0999             nan     0.0500   -0.0008
##     80        3.6296             nan     0.0500   -0.0071
##    100        3.3475             nan     0.0500   -0.0137
##    120        3.1169             nan     0.0500   -0.0224
##    140        2.9306             nan     0.0500   -0.0156
##    160        2.7564             nan     0.0500   -0.0088
##    180        2.6169             nan     0.0500   -0.0187
##    200        2.4813             nan     0.0500   -0.0045
##    220        2.3481             nan     0.0500   -0.0080
##    240        2.2431             nan     0.0500   -0.0033
##    260        2.1423             nan     0.0500   -0.0151
##    280        2.0568             nan     0.0500   -0.0260
##    300        1.9780             nan     0.0500   -0.0131
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.4942             nan     0.1000    7.4367
##      2       48.1030             nan     0.1000    6.0468
##      3       43.1471             nan     0.1000    4.8749
##      4       39.1182             nan     0.1000    4.1506
##      5       35.2624             nan     0.1000    3.8812
##      6       32.0207             nan     0.1000    3.1352
##      7       29.1942             nan     0.1000    2.7367
##      8       26.4993             nan     0.1000    2.3297
##      9       24.4903             nan     0.1000    1.9794
##     10       22.6163             nan     0.1000    1.4241
##     20       11.6790             nan     0.1000    0.4447
##     40        5.9052             nan     0.1000    0.1023
##     60        4.7377             nan     0.1000    0.0111
##     80        4.3624             nan     0.1000   -0.0610
##    100        4.1634             nan     0.1000    0.0030
##    120        3.9861             nan     0.1000   -0.0143
##    140        3.8581             nan     0.1000    0.0050
##    160        3.7432             nan     0.1000   -0.0134
##    180        3.6361             nan     0.1000    0.0018
##    200        3.5595             nan     0.1000   -0.0672
##    220        3.4629             nan     0.1000   -0.0114
##    240        3.3850             nan     0.1000   -0.0203
##    260        3.3223             nan     0.1000   -0.0070
##    280        3.2471             nan     0.1000   -0.0141
##    300        3.1868             nan     0.1000   -0.0422
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.3703             nan     0.1000    7.5508
##      2       48.1661             nan     0.1000    5.7058
##      3       42.9969             nan     0.1000    5.6435
##      4       38.7920             nan     0.1000    3.8153
##      5       35.0737             nan     0.1000    3.4045
##      6       31.5012             nan     0.1000    3.1884
##      7       28.3632             nan     0.1000    2.8261
##      8       25.9607             nan     0.1000    2.2638
##      9       23.9589             nan     0.1000    2.2215
##     10       22.1517             nan     0.1000    1.7520
##     20       11.4952             nan     0.1000    0.5707
##     40        6.0919             nan     0.1000    0.0186
##     60        4.8302             nan     0.1000    0.0059
##     80        4.5014             nan     0.1000   -0.0423
##    100        4.2902             nan     0.1000   -0.0120
##    120        4.1085             nan     0.1000   -0.0136
##    140        3.9673             nan     0.1000   -0.0211
##    160        3.8808             nan     0.1000   -0.0444
##    180        3.7951             nan     0.1000   -0.0263
##    200        3.6965             nan     0.1000   -0.0063
##    220        3.6136             nan     0.1000   -0.0085
##    240        3.5486             nan     0.1000   -0.0316
##    260        3.4780             nan     0.1000   -0.0060
##    280        3.4113             nan     0.1000   -0.0132
##    300        3.3488             nan     0.1000   -0.0232
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.5642             nan     0.1000    7.3150
##      2       48.3378             nan     0.1000    6.4479
##      3       42.7220             nan     0.1000    5.3661
##      4       38.3344             nan     0.1000    4.1302
##      5       34.7347             nan     0.1000    3.7821
##      6       31.4075             nan     0.1000    3.1892
##      7       28.9008             nan     0.1000    2.4299
##      8       26.3632             nan     0.1000    1.9049
##      9       24.3865             nan     0.1000    2.0272
##     10       22.5833             nan     0.1000    1.7961
##     20       11.9961             nan     0.1000    0.5546
##     40        6.0683             nan     0.1000    0.0527
##     60        4.8348             nan     0.1000   -0.0322
##     80        4.5493             nan     0.1000   -0.0050
##    100        4.3309             nan     0.1000   -0.0095
##    120        4.1797             nan     0.1000   -0.0638
##    140        4.0479             nan     0.1000   -0.0234
##    160        3.9653             nan     0.1000   -0.0235
##    180        3.8935             nan     0.1000   -0.0112
##    200        3.8346             nan     0.1000   -0.0406
##    220        3.7815             nan     0.1000   -0.0161
##    240        3.7158             nan     0.1000   -0.0452
##    260        3.6649             nan     0.1000   -0.0427
##    280        3.6115             nan     0.1000   -0.0052
##    300        3.5601             nan     0.1000   -0.0305
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.0842             nan     0.1000    9.3246
##      2       44.1789             nan     0.1000    7.8135
##      3       38.0786             nan     0.1000    5.3407
##      4       32.9572             nan     0.1000    5.6217
##      5       28.3216             nan     0.1000    4.1822
##      6       24.5225             nan     0.1000    3.5355
##      7       21.6613             nan     0.1000    3.0743
##      8       18.9192             nan     0.1000    2.6779
##      9       16.9583             nan     0.1000    2.0803
##     10       15.0642             nan     0.1000    1.8737
##     20        6.4147             nan     0.1000    0.3294
##     40        3.7682             nan     0.1000   -0.0453
##     60        3.1294             nan     0.1000   -0.0218
##     80        2.6326             nan     0.1000   -0.0061
##    100        2.3285             nan     0.1000   -0.0259
##    120        2.0895             nan     0.1000   -0.0145
##    140        1.8711             nan     0.1000   -0.0135
##    160        1.6898             nan     0.1000   -0.0104
##    180        1.5611             nan     0.1000   -0.0145
##    200        1.4394             nan     0.1000   -0.0098
##    220        1.3228             nan     0.1000   -0.0275
##    240        1.2025             nan     0.1000   -0.0196
##    260        1.1222             nan     0.1000   -0.0112
##    280        1.0373             nan     0.1000   -0.0068
##    300        0.9568             nan     0.1000   -0.0068
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.2851             nan     0.1000    9.4219
##      2       43.8049             nan     0.1000    6.9725
##      3       37.1925             nan     0.1000    6.5238
##      4       32.1329             nan     0.1000    4.1976
##      5       27.7888             nan     0.1000    3.9423
##      6       24.1978             nan     0.1000    2.5259
##      7       21.2532             nan     0.1000    2.8739
##      8       18.6324             nan     0.1000    2.4699
##      9       16.3798             nan     0.1000    1.4447
##     10       14.3329             nan     0.1000    1.8704
##     20        6.4610             nan     0.1000    0.2738
##     40        3.7799             nan     0.1000   -0.0184
##     60        3.1996             nan     0.1000   -0.0145
##     80        2.8827             nan     0.1000   -0.0418
##    100        2.6678             nan     0.1000   -0.0124
##    120        2.4172             nan     0.1000   -0.0390
##    140        2.2186             nan     0.1000   -0.0341
##    160        2.0577             nan     0.1000   -0.0232
##    180        1.9114             nan     0.1000   -0.0251
##    200        1.7889             nan     0.1000   -0.0183
##    220        1.6888             nan     0.1000   -0.0361
##    240        1.5788             nan     0.1000   -0.0148
##    260        1.5009             nan     0.1000   -0.0247
##    280        1.4380             nan     0.1000   -0.0233
##    300        1.3397             nan     0.1000   -0.0095
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.2246             nan     0.1000   10.4952
##      2       44.9604             nan     0.1000    7.0779
##      3       38.0291             nan     0.1000    6.3631
##      4       33.0004             nan     0.1000    5.4854
##      5       28.2929             nan     0.1000    3.8314
##      6       24.5448             nan     0.1000    3.5218
##      7       21.5809             nan     0.1000    3.0567
##      8       19.0817             nan     0.1000    2.0424
##      9       16.8944             nan     0.1000    2.1184
##     10       15.1068             nan     0.1000    1.7025
##     20        6.5239             nan     0.1000    0.2687
##     40        4.0757             nan     0.1000   -0.0082
##     60        3.6032             nan     0.1000   -0.0299
##     80        3.2191             nan     0.1000   -0.0143
##    100        2.9313             nan     0.1000   -0.0232
##    120        2.7037             nan     0.1000   -0.0126
##    140        2.5192             nan     0.1000   -0.0098
##    160        2.3595             nan     0.1000   -0.0099
##    180        2.2522             nan     0.1000   -0.0288
##    200        2.1502             nan     0.1000   -0.0191
##    220        2.0633             nan     0.1000   -0.0290
##    240        1.9872             nan     0.1000   -0.0178
##    260        1.8956             nan     0.1000   -0.0396
##    280        1.8139             nan     0.1000   -0.0134
##    300        1.7371             nan     0.1000   -0.0157
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.4508             nan     0.1000    9.6105
##      2       42.7810             nan     0.1000    7.7365
##      3       35.9116             nan     0.1000    7.0000
##      4       30.1271             nan     0.1000    5.4712
##      5       25.6356             nan     0.1000    4.3297
##      6       21.7710             nan     0.1000    3.0801
##      7       18.9673             nan     0.1000    3.0326
##      8       16.1625             nan     0.1000    2.6169
##      9       14.1362             nan     0.1000    1.8250
##     10       12.5215             nan     0.1000    1.3520
##     20        5.0271             nan     0.1000    0.2604
##     40        2.8100             nan     0.1000   -0.0053
##     60        2.1302             nan     0.1000   -0.0253
##     80        1.8126             nan     0.1000   -0.0342
##    100        1.5325             nan     0.1000   -0.0089
##    120        1.3051             nan     0.1000   -0.0288
##    140        1.0968             nan     0.1000   -0.0055
##    160        0.9408             nan     0.1000   -0.0174
##    180        0.8326             nan     0.1000   -0.0207
##    200        0.7318             nan     0.1000   -0.0101
##    220        0.6465             nan     0.1000   -0.0093
##    240        0.5778             nan     0.1000   -0.0209
##    260        0.5145             nan     0.1000   -0.0096
##    280        0.4642             nan     0.1000   -0.0078
##    300        0.4211             nan     0.1000   -0.0088
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.8478             nan     0.1000    9.4693
##      2       43.7489             nan     0.1000    8.0990
##      3       36.4801             nan     0.1000    5.8398
##      4       30.9473             nan     0.1000    5.3816
##      5       26.2613             nan     0.1000    4.2821
##      6       22.4795             nan     0.1000    3.1843
##      7       19.3738             nan     0.1000    2.8526
##      8       16.5866             nan     0.1000    2.2636
##      9       14.5540             nan     0.1000    1.8696
##     10       12.7029             nan     0.1000    1.6632
##     20        5.2177             nan     0.1000    0.1820
##     40        3.1737             nan     0.1000   -0.0396
##     60        2.6599             nan     0.1000    0.0013
##     80        2.3341             nan     0.1000   -0.0461
##    100        2.0495             nan     0.1000   -0.0389
##    120        1.8359             nan     0.1000   -0.0479
##    140        1.6264             nan     0.1000   -0.0224
##    160        1.4851             nan     0.1000   -0.0165
##    180        1.3316             nan     0.1000   -0.0085
##    200        1.2032             nan     0.1000   -0.0142
##    220        1.1055             nan     0.1000   -0.0185
##    240        1.0264             nan     0.1000   -0.0125
##    260        0.9342             nan     0.1000   -0.0153
##    280        0.8672             nan     0.1000   -0.0285
##    300        0.8035             nan     0.1000   -0.0138
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.9098             nan     0.1000   11.5385
##      2       42.7999             nan     0.1000    8.4313
##      3       36.1975             nan     0.1000    6.2216
##      4       30.6846             nan     0.1000    5.3458
##      5       26.2712             nan     0.1000    4.3463
##      6       22.6375             nan     0.1000    3.3744
##      7       19.4777             nan     0.1000    2.9858
##      8       16.8529             nan     0.1000    2.6947
##      9       14.7269             nan     0.1000    2.2670
##     10       12.8792             nan     0.1000    1.8642
##     20        5.5631             nan     0.1000    0.2767
##     40        3.6375             nan     0.1000   -0.0077
##     60        3.0863             nan     0.1000   -0.0401
##     80        2.7156             nan     0.1000   -0.0403
##    100        2.4505             nan     0.1000   -0.0594
##    120        2.2095             nan     0.1000   -0.0396
##    140        2.0110             nan     0.1000   -0.0229
##    160        1.8691             nan     0.1000   -0.0299
##    180        1.7029             nan     0.1000   -0.0270
##    200        1.5728             nan     0.1000   -0.0415
##    220        1.4702             nan     0.1000   -0.0198
##    240        1.3920             nan     0.1000   -0.0149
##    260        1.2846             nan     0.1000   -0.0267
##    280        1.2200             nan     0.1000   -0.0155
##    300        1.1637             nan     0.1000   -0.0109
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.6823             nan     0.0100    0.7368
##      2       58.9940             nan     0.0100    0.7574
##      3       58.2190             nan     0.0100    0.7148
##      4       57.5109             nan     0.0100    0.7034
##      5       56.7842             nan     0.0100    0.7185
##      6       56.0852             nan     0.0100    0.6905
##      7       55.4319             nan     0.0100    0.6603
##      8       54.7807             nan     0.0100    0.6777
##      9       54.0980             nan     0.0100    0.6482
##     10       53.4720             nan     0.0100    0.6282
##     20       47.4933             nan     0.0100    0.5268
##     40       38.3015             nan     0.0100    0.3697
##     60       31.4362             nan     0.0100    0.2878
##     80       26.1281             nan     0.0100    0.2077
##    100       22.0564             nan     0.0100    0.1724
##    120       18.9299             nan     0.0100    0.1284
##    140       16.4093             nan     0.0100    0.0435
##    160       14.3666             nan     0.0100    0.0787
##    180       12.7596             nan     0.0100    0.0684
##    200       11.4469             nan     0.0100    0.0551
##    220       10.3126             nan     0.0100    0.0414
##    240        9.4292             nan     0.0100    0.0337
##    260        8.6507             nan     0.0100    0.0331
##    280        8.0116             nan     0.0100    0.0139
##    300        7.4394             nan     0.0100    0.0091
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.6100             nan     0.0100    0.7405
##      2       58.7868             nan     0.0100    0.7641
##      3       57.9979             nan     0.0100    0.7232
##      4       57.2784             nan     0.0100    0.6819
##      5       56.6536             nan     0.0100    0.6686
##      6       55.9197             nan     0.0100    0.6963
##      7       55.1589             nan     0.0100    0.6507
##      8       54.4953             nan     0.0100    0.6717
##      9       53.8667             nan     0.0100    0.6275
##     10       53.2052             nan     0.0100    0.5868
##     20       47.3637             nan     0.0100    0.5249
##     40       38.3072             nan     0.0100    0.3386
##     60       31.4029             nan     0.0100    0.2052
##     80       26.1564             nan     0.0100    0.2181
##    100       21.9544             nan     0.0100    0.1785
##    120       18.8067             nan     0.0100    0.1058
##    140       16.3365             nan     0.0100    0.0915
##    160       14.3781             nan     0.0100    0.0808
##    180       12.7709             nan     0.0100    0.0517
##    200       11.4239             nan     0.0100    0.0570
##    220       10.3280             nan     0.0100    0.0619
##    240        9.4318             nan     0.0100    0.0239
##    260        8.6486             nan     0.0100    0.0196
##    280        7.9889             nan     0.0100    0.0245
##    300        7.4131             nan     0.0100    0.0191
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.6694             nan     0.0100    0.7677
##      2       58.9584             nan     0.0100    0.7666
##      3       58.1586             nan     0.0100    0.7000
##      4       57.5024             nan     0.0100    0.6179
##      5       56.8155             nan     0.0100    0.7314
##      6       56.1038             nan     0.0100    0.6908
##      7       55.3817             nan     0.0100    0.6575
##      8       54.7487             nan     0.0100    0.6695
##      9       54.0907             nan     0.0100    0.6266
##     10       53.4235             nan     0.0100    0.6263
##     20       47.6873             nan     0.0100    0.5010
##     40       38.4080             nan     0.0100    0.3874
##     60       31.3738             nan     0.0100    0.3018
##     80       26.1046             nan     0.0100    0.2256
##    100       22.0431             nan     0.0100    0.1820
##    120       18.8617             nan     0.0100    0.1221
##    140       16.3776             nan     0.0100    0.1023
##    160       14.4163             nan     0.0100    0.0582
##    180       12.8240             nan     0.0100    0.0686
##    200       11.5007             nan     0.0100    0.0537
##    220       10.3860             nan     0.0100    0.0481
##    240        9.5198             nan     0.0100    0.0288
##    260        8.7751             nan     0.0100    0.0267
##    280        8.1175             nan     0.0100    0.0248
##    300        7.5633             nan     0.0100    0.0173
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.4537             nan     0.0100    0.8790
##      2       58.5173             nan     0.0100    0.8837
##      3       57.5514             nan     0.0100    0.8984
##      4       56.6026             nan     0.0100    0.8982
##      5       55.6625             nan     0.0100    0.8371
##      6       54.7812             nan     0.0100    0.8885
##      7       53.8758             nan     0.0100    0.8326
##      8       53.0011             nan     0.0100    0.9246
##      9       52.1731             nan     0.0100    0.8596
##     10       51.3352             nan     0.0100    0.7578
##     20       43.6794             nan     0.0100    0.6195
##     40       32.2301             nan     0.0100    0.4822
##     60       24.2577             nan     0.0100    0.3160
##     80       18.6253             nan     0.0100    0.2283
##    100       14.6195             nan     0.0100    0.1435
##    120       11.7307             nan     0.0100    0.1277
##    140        9.5705             nan     0.0100    0.0827
##    160        8.0502             nan     0.0100    0.0475
##    180        6.9022             nan     0.0100    0.0449
##    200        6.0159             nan     0.0100    0.0300
##    220        5.3758             nan     0.0100    0.0240
##    240        4.8923             nan     0.0100    0.0122
##    260        4.5289             nan     0.0100    0.0105
##    280        4.2590             nan     0.0100    0.0054
##    300        4.0390             nan     0.0100    0.0027
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.4854             nan     0.0100    1.0315
##      2       58.5040             nan     0.0100    0.9146
##      3       57.5697             nan     0.0100    0.8463
##      4       56.6588             nan     0.0100    0.9269
##      5       55.7566             nan     0.0100    0.7372
##      6       54.8282             nan     0.0100    0.9267
##      7       53.9807             nan     0.0100    0.9041
##      8       53.0988             nan     0.0100    0.8590
##      9       52.2335             nan     0.0100    0.7957
##     10       51.4233             nan     0.0100    0.7834
##     20       43.8350             nan     0.0100    0.6357
##     40       32.2412             nan     0.0100    0.4445
##     60       24.1629             nan     0.0100    0.2989
##     80       18.6372             nan     0.0100    0.2468
##    100       14.6186             nan     0.0100    0.1670
##    120       11.7218             nan     0.0100    0.1286
##    140        9.6425             nan     0.0100    0.0816
##    160        8.1241             nan     0.0100    0.0562
##    180        6.9762             nan     0.0100    0.0245
##    200        6.1034             nan     0.0100    0.0351
##    220        5.4673             nan     0.0100    0.0159
##    240        4.9795             nan     0.0100    0.0145
##    260        4.5980             nan     0.0100    0.0083
##    280        4.3341             nan     0.0100    0.0057
##    300        4.1309             nan     0.0100    0.0039
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.3985             nan     0.0100    0.9423
##      2       58.4228             nan     0.0100    1.0260
##      3       57.4807             nan     0.0100    0.8263
##      4       56.5449             nan     0.0100    1.0227
##      5       55.6241             nan     0.0100    0.8614
##      6       54.7140             nan     0.0100    0.9334
##      7       53.8096             nan     0.0100    0.8481
##      8       53.0135             nan     0.0100    0.8680
##      9       52.1557             nan     0.0100    0.8153
##     10       51.3196             nan     0.0100    0.8642
##     20       43.6644             nan     0.0100    0.6664
##     40       32.1502             nan     0.0100    0.4758
##     60       24.3324             nan     0.0100    0.2923
##     80       18.6202             nan     0.0100    0.2149
##    100       14.6901             nan     0.0100    0.1642
##    120       11.9079             nan     0.0100    0.0740
##    140        9.9109             nan     0.0100    0.0675
##    160        8.3872             nan     0.0100    0.0491
##    180        7.2240             nan     0.0100    0.0454
##    200        6.4056             nan     0.0100    0.0321
##    220        5.7914             nan     0.0100    0.0203
##    240        5.3270             nan     0.0100    0.0153
##    260        4.9732             nan     0.0100    0.0131
##    280        4.6992             nan     0.0100    0.0058
##    300        4.4856             nan     0.0100    0.0045
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.3399             nan     0.0100    1.0410
##      2       58.3287             nan     0.0100    0.9834
##      3       57.2252             nan     0.0100    1.1228
##      4       56.2826             nan     0.0100    0.8837
##      5       55.2901             nan     0.0100    0.9723
##      6       54.3333             nan     0.0100    0.9093
##      7       53.3809             nan     0.0100    0.8958
##      8       52.4560             nan     0.0100    0.9086
##      9       51.6023             nan     0.0100    0.8679
##     10       50.7378             nan     0.0100    0.8417
##     20       42.8202             nan     0.0100    0.7165
##     40       30.9214             nan     0.0100    0.4491
##     60       22.7483             nan     0.0100    0.3333
##     80       16.9485             nan     0.0100    0.2021
##    100       12.8695             nan     0.0100    0.1425
##    120       10.0022             nan     0.0100    0.0966
##    140        8.0450             nan     0.0100    0.0707
##    160        6.6209             nan     0.0100    0.0568
##    180        5.5803             nan     0.0100    0.0299
##    200        4.8577             nan     0.0100    0.0196
##    220        4.3469             nan     0.0100    0.0168
##    240        3.9497             nan     0.0100    0.0069
##    260        3.6457             nan     0.0100    0.0050
##    280        3.4158             nan     0.0100    0.0049
##    300        3.2262             nan     0.0100   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.3679             nan     0.0100    1.0587
##      2       58.3027             nan     0.0100    1.0086
##      3       57.2678             nan     0.0100    1.0492
##      4       56.2529             nan     0.0100    0.9949
##      5       55.3397             nan     0.0100    1.0062
##      6       54.3528             nan     0.0100    0.8128
##      7       53.4004             nan     0.0100    0.9332
##      8       52.4631             nan     0.0100    0.9586
##      9       51.5575             nan     0.0100    0.9106
##     10       50.6794             nan     0.0100    0.9015
##     20       42.6847             nan     0.0100    0.6385
##     40       30.6552             nan     0.0100    0.5083
##     60       22.4710             nan     0.0100    0.3535
##     80       16.8240             nan     0.0100    0.2058
##    100       12.8683             nan     0.0100    0.1474
##    120       10.0577             nan     0.0100    0.1039
##    140        8.1552             nan     0.0100    0.0756
##    160        6.7648             nan     0.0100    0.0619
##    180        5.7506             nan     0.0100    0.0338
##    200        5.0544             nan     0.0100    0.0254
##    220        4.5420             nan     0.0100    0.0140
##    240        4.1540             nan     0.0100    0.0048
##    260        3.8496             nan     0.0100    0.0045
##    280        3.6260             nan     0.0100    0.0008
##    300        3.4483             nan     0.0100    0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.3479             nan     0.0100    1.0742
##      2       58.3655             nan     0.0100    1.0188
##      3       57.3481             nan     0.0100    0.9436
##      4       56.3870             nan     0.0100    0.9702
##      5       55.4357             nan     0.0100    0.8858
##      6       54.5114             nan     0.0100    0.8888
##      7       53.5687             nan     0.0100    0.8814
##      8       52.6887             nan     0.0100    0.8102
##      9       51.7623             nan     0.0100    0.9391
##     10       50.8459             nan     0.0100    0.7667
##     20       43.0407             nan     0.0100    0.7046
##     40       31.0539             nan     0.0100    0.5283
##     60       22.9464             nan     0.0100    0.3422
##     80       17.2216             nan     0.0100    0.2431
##    100       13.2803             nan     0.0100    0.1444
##    120       10.4537             nan     0.0100    0.1020
##    140        8.5716             nan     0.0100    0.0816
##    160        7.1649             nan     0.0100    0.0433
##    180        6.1610             nan     0.0100    0.0347
##    200        5.4470             nan     0.0100    0.0252
##    220        4.9239             nan     0.0100    0.0133
##    240        4.5613             nan     0.0100    0.0031
##    260        4.2796             nan     0.0100    0.0029
##    280        4.0610             nan     0.0100    0.0046
##    300        3.9013             nan     0.0100    0.0034
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.6261             nan     0.0500    3.7487
##      2       53.7912             nan     0.0500    2.7395
##      3       50.7649             nan     0.0500    3.2602
##      4       47.8375             nan     0.0500    3.0468
##      5       45.3476             nan     0.0500    2.6203
##      6       42.9063             nan     0.0500    2.3380
##      7       40.5140             nan     0.0500    2.2158
##      8       38.3436             nan     0.0500    2.0480
##      9       36.3411             nan     0.0500    2.3154
##     10       34.4406             nan     0.0500    1.6555
##     20       21.5898             nan     0.0500    0.8591
##     40       11.0134             nan     0.0500    0.2203
##     60        7.3240             nan     0.0500    0.0550
##     80        5.6272             nan     0.0500    0.0217
##    100        4.8134             nan     0.0500    0.0298
##    120        4.4378             nan     0.0500   -0.0111
##    140        4.2641             nan     0.0500   -0.0120
##    160        4.1355             nan     0.0500   -0.0072
##    180        4.0309             nan     0.0500   -0.0101
##    200        3.9671             nan     0.0500   -0.0051
##    220        3.8848             nan     0.0500   -0.0197
##    240        3.8219             nan     0.0500   -0.0204
##    260        3.7512             nan     0.0500   -0.0056
##    280        3.6941             nan     0.0500   -0.0100
##    300        3.6495             nan     0.0500   -0.0182
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.8668             nan     0.0500    3.5521
##      2       53.5744             nan     0.0500    3.2931
##      3       50.4557             nan     0.0500    2.8791
##      4       47.4603             nan     0.0500    2.8969
##      5       44.8641             nan     0.0500    2.4955
##      6       42.5790             nan     0.0500    2.3619
##      7       40.2903             nan     0.0500    2.1625
##      8       38.0764             nan     0.0500    1.9872
##      9       36.1914             nan     0.0500    1.7092
##     10       34.4225             nan     0.0500    1.4870
##     20       21.6748             nan     0.0500    0.8449
##     40       11.3266             nan     0.0500    0.2378
##     60        7.4220             nan     0.0500    0.0895
##     80        5.6884             nan     0.0500    0.0698
##    100        4.8437             nan     0.0500   -0.0277
##    120        4.4746             nan     0.0500   -0.0180
##    140        4.3092             nan     0.0500   -0.0166
##    160        4.1675             nan     0.0500    0.0011
##    180        4.0536             nan     0.0500   -0.0089
##    200        3.9674             nan     0.0500    0.0001
##    220        3.9136             nan     0.0500   -0.0048
##    240        3.8534             nan     0.0500   -0.0077
##    260        3.8007             nan     0.0500   -0.0060
##    280        3.7443             nan     0.0500   -0.0072
##    300        3.6985             nan     0.0500   -0.0055
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.5783             nan     0.0500    3.6804
##      2       53.1842             nan     0.0500    3.1426
##      3       50.0170             nan     0.0500    3.0461
##      4       47.1402             nan     0.0500    2.6536
##      5       44.5611             nan     0.0500    2.5262
##      6       42.0138             nan     0.0500    2.5241
##      7       39.7197             nan     0.0500    1.9784
##      8       37.7440             nan     0.0500    2.0082
##      9       35.8590             nan     0.0500    1.7508
##     10       34.0054             nan     0.0500    1.5630
##     20       21.5637             nan     0.0500    0.7070
##     40       11.2986             nan     0.0500    0.3113
##     60        7.4859             nan     0.0500    0.1079
##     80        5.8868             nan     0.0500    0.0385
##    100        5.1682             nan     0.0500    0.0113
##    120        4.8127             nan     0.0500   -0.0073
##    140        4.6199             nan     0.0500    0.0005
##    160        4.4894             nan     0.0500   -0.0013
##    180        4.3780             nan     0.0500   -0.0085
##    200        4.2904             nan     0.0500   -0.0062
##    220        4.2179             nan     0.0500   -0.0164
##    240        4.1445             nan     0.0500   -0.0062
##    260        4.0778             nan     0.0500   -0.0051
##    280        4.0302             nan     0.0500   -0.0204
##    300        3.9868             nan     0.0500   -0.0134
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.9390             nan     0.0500    4.5195
##      2       51.3501             nan     0.0500    4.3905
##      3       47.3501             nan     0.0500    3.3966
##      4       43.4784             nan     0.0500    3.4441
##      5       40.0792             nan     0.0500    3.3657
##      6       37.1872             nan     0.0500    2.7718
##      7       34.4842             nan     0.0500    2.8705
##      8       31.8459             nan     0.0500    2.2082
##      9       29.4891             nan     0.0500    2.2506
##     10       27.3931             nan     0.0500    2.0703
##     20       14.2410             nan     0.0500    0.7321
##     40        6.0520             nan     0.0500    0.1605
##     60        4.1846             nan     0.0500    0.0145
##     80        3.4837             nan     0.0500    0.0023
##    100        3.1241             nan     0.0500   -0.0041
##    120        2.8952             nan     0.0500   -0.0096
##    140        2.7131             nan     0.0500   -0.0124
##    160        2.5656             nan     0.0500   -0.0012
##    180        2.4045             nan     0.0500   -0.0087
##    200        2.2753             nan     0.0500   -0.0159
##    220        2.1630             nan     0.0500   -0.0185
##    240        2.0601             nan     0.0500   -0.0090
##    260        1.9821             nan     0.0500   -0.0217
##    280        1.8794             nan     0.0500   -0.0135
##    300        1.8027             nan     0.0500   -0.0101
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.7343             nan     0.0500    4.8378
##      2       51.0956             nan     0.0500    3.9425
##      3       47.1105             nan     0.0500    4.1542
##      4       43.6623             nan     0.0500    3.4586
##      5       40.3779             nan     0.0500    3.2234
##      6       37.4336             nan     0.0500    2.8433
##      7       34.6588             nan     0.0500    3.1844
##      8       32.0552             nan     0.0500    2.2884
##      9       29.5180             nan     0.0500    2.4060
##     10       27.3576             nan     0.0500    1.9554
##     20       14.0868             nan     0.0500    0.7854
##     40        6.0087             nan     0.0500    0.1386
##     60        4.0308             nan     0.0500    0.0192
##     80        3.4844             nan     0.0500   -0.0071
##    100        3.1757             nan     0.0500    0.0057
##    120        2.9451             nan     0.0500   -0.0117
##    140        2.7599             nan     0.0500   -0.0066
##    160        2.6131             nan     0.0500   -0.0043
##    180        2.4732             nan     0.0500   -0.0046
##    200        2.3528             nan     0.0500   -0.0104
##    220        2.2511             nan     0.0500   -0.0119
##    240        2.1719             nan     0.0500   -0.0064
##    260        2.0978             nan     0.0500   -0.0170
##    280        2.0266             nan     0.0500   -0.0138
##    300        1.9598             nan     0.0500   -0.0075
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.7425             nan     0.0500    4.6921
##      2       51.3009             nan     0.0500    4.1635
##      3       47.3791             nan     0.0500    4.1524
##      4       43.8733             nan     0.0500    3.7272
##      5       40.5440             nan     0.0500    3.4299
##      6       37.4942             nan     0.0500    2.9157
##      7       34.7647             nan     0.0500    2.5271
##      8       32.2965             nan     0.0500    2.3437
##      9       29.8578             nan     0.0500    2.4670
##     10       27.6123             nan     0.0500    1.8093
##     20       14.5650             nan     0.0500    0.7572
##     40        6.3284             nan     0.0500    0.0840
##     60        4.4073             nan     0.0500    0.0195
##     80        3.8088             nan     0.0500    0.0057
##    100        3.5520             nan     0.0500   -0.0171
##    120        3.3050             nan     0.0500   -0.0224
##    140        3.1280             nan     0.0500   -0.0052
##    160        2.9630             nan     0.0500   -0.0273
##    180        2.8684             nan     0.0500   -0.0183
##    200        2.7673             nan     0.0500   -0.0063
##    220        2.6688             nan     0.0500   -0.0161
##    240        2.5867             nan     0.0500   -0.0197
##    260        2.5087             nan     0.0500   -0.0186
##    280        2.4237             nan     0.0500   -0.0203
##    300        2.3569             nan     0.0500   -0.0124
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.0482             nan     0.0500    5.3768
##      2       50.5553             nan     0.0500    4.6181
##      3       46.3952             nan     0.0500    4.0937
##      4       42.3138             nan     0.0500    3.9664
##      5       38.9461             nan     0.0500    3.0012
##      6       35.8536             nan     0.0500    2.9039
##      7       33.1376             nan     0.0500    2.6641
##      8       30.4637             nan     0.0500    2.3516
##      9       28.0884             nan     0.0500    2.2834
##     10       25.8278             nan     0.0500    2.2553
##     20       12.5306             nan     0.0500    0.8718
##     40        4.6861             nan     0.0500    0.1295
##     60        3.1509             nan     0.0500   -0.0100
##     80        2.6414             nan     0.0500   -0.0136
##    100        2.3102             nan     0.0500   -0.0137
##    120        2.0831             nan     0.0500   -0.0093
##    140        1.8696             nan     0.0500   -0.0117
##    160        1.6804             nan     0.0500   -0.0140
##    180        1.5325             nan     0.0500   -0.0080
##    200        1.4241             nan     0.0500   -0.0184
##    220        1.3063             nan     0.0500   -0.0132
##    240        1.2183             nan     0.0500   -0.0078
##    260        1.1336             nan     0.0500   -0.0098
##    280        1.0626             nan     0.0500   -0.0084
##    300        0.9816             nan     0.0500   -0.0084
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.1561             nan     0.0500    5.0999
##      2       50.5189             nan     0.0500    4.2868
##      3       46.2841             nan     0.0500    4.5820
##      4       42.6140             nan     0.0500    3.6570
##      5       39.0915             nan     0.0500    3.3062
##      6       36.0379             nan     0.0500    3.1714
##      7       32.8576             nan     0.0500    2.9433
##      8       30.2185             nan     0.0500    2.6580
##      9       27.8724             nan     0.0500    2.4005
##     10       25.7293             nan     0.0500    1.9413
##     20       12.5623             nan     0.0500    0.7227
##     40        4.9418             nan     0.0500    0.1395
##     60        3.4713             nan     0.0500   -0.0119
##     80        2.9431             nan     0.0500   -0.0307
##    100        2.6732             nan     0.0500   -0.0293
##    120        2.4515             nan     0.0500   -0.0147
##    140        2.2972             nan     0.0500   -0.0204
##    160        2.1346             nan     0.0500   -0.0275
##    180        1.9714             nan     0.0500   -0.0118
##    200        1.8353             nan     0.0500   -0.0234
##    220        1.7369             nan     0.0500   -0.0092
##    240        1.6407             nan     0.0500   -0.0108
##    260        1.5505             nan     0.0500   -0.0085
##    280        1.4689             nan     0.0500   -0.0135
##    300        1.3887             nan     0.0500   -0.0085
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.0419             nan     0.0500    5.1156
##      2       50.2492             nan     0.0500    4.5451
##      3       46.2944             nan     0.0500    3.9689
##      4       42.3943             nan     0.0500    4.0668
##      5       38.9155             nan     0.0500    3.1312
##      6       35.8146             nan     0.0500    2.7849
##      7       32.8144             nan     0.0500    2.6969
##      8       30.3687             nan     0.0500    2.4560
##      9       28.0512             nan     0.0500    2.3712
##     10       25.9431             nan     0.0500    2.1032
##     20       12.7705             nan     0.0500    0.7598
##     40        5.2424             nan     0.0500    0.1559
##     60        3.8161             nan     0.0500   -0.0034
##     80        3.3745             nan     0.0500   -0.0119
##    100        3.0944             nan     0.0500   -0.0002
##    120        2.8843             nan     0.0500   -0.0151
##    140        2.7255             nan     0.0500   -0.0002
##    160        2.5864             nan     0.0500   -0.0183
##    180        2.4316             nan     0.0500   -0.0128
##    200        2.2901             nan     0.0500   -0.0063
##    220        2.1765             nan     0.0500   -0.0167
##    240        2.0934             nan     0.0500   -0.0136
##    260        2.0053             nan     0.0500   -0.0113
##    280        1.9271             nan     0.0500   -0.0223
##    300        1.8562             nan     0.0500   -0.0181
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.0272             nan     0.1000    7.3389
##      2       46.9954             nan     0.1000    5.7975
##      3       42.0666             nan     0.1000    4.8178
##      4       37.9822             nan     0.1000    4.1102
##      5       34.1106             nan     0.1000    3.7522
##      6       31.0223             nan     0.1000    2.7855
##      7       27.8301             nan     0.1000    2.7865
##      8       25.4326             nan     0.1000    2.3715
##      9       23.6111             nan     0.1000    1.9434
##     10       21.8393             nan     0.1000    1.5203
##     20       11.2842             nan     0.1000    0.4453
##     40        5.8421             nan     0.1000    0.0947
##     60        4.7185             nan     0.1000    0.0019
##     80        4.4229             nan     0.1000   -0.0531
##    100        4.2245             nan     0.1000   -0.0037
##    120        4.0512             nan     0.1000   -0.0104
##    140        3.9244             nan     0.1000   -0.0365
##    160        3.8124             nan     0.1000   -0.0235
##    180        3.7065             nan     0.1000   -0.0178
##    200        3.6129             nan     0.1000   -0.0115
##    220        3.5099             nan     0.1000   -0.0344
##    240        3.4259             nan     0.1000   -0.0262
##    260        3.3584             nan     0.1000   -0.0170
##    280        3.2961             nan     0.1000   -0.0191
##    300        3.2404             nan     0.1000   -0.0209
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.0410             nan     0.1000    6.9696
##      2       46.7956             nan     0.1000    5.8817
##      3       41.3360             nan     0.1000    5.2099
##      4       36.7949             nan     0.1000    3.7840
##      5       33.6464             nan     0.1000    3.1577
##      6       30.4385             nan     0.1000    2.9681
##      7       27.8181             nan     0.1000    2.5351
##      8       24.9692             nan     0.1000    2.3506
##      9       22.7097             nan     0.1000    2.1194
##     10       20.8603             nan     0.1000    1.9698
##     20       11.0603             nan     0.1000    0.5672
##     40        5.5132             nan     0.1000    0.0774
##     60        4.5051             nan     0.1000    0.0077
##     80        4.1886             nan     0.1000   -0.0056
##    100        4.0222             nan     0.1000   -0.0118
##    120        3.8851             nan     0.1000   -0.0125
##    140        3.7724             nan     0.1000   -0.0125
##    160        3.6565             nan     0.1000   -0.0092
##    180        3.5716             nan     0.1000   -0.0101
##    200        3.4841             nan     0.1000   -0.0455
##    220        3.4304             nan     0.1000   -0.0054
##    240        3.3541             nan     0.1000   -0.0091
##    260        3.3104             nan     0.1000   -0.0223
##    280        3.2504             nan     0.1000   -0.0040
##    300        3.2099             nan     0.1000   -0.0253
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.0709             nan     0.1000    7.3321
##      2       47.0443             nan     0.1000    5.4503
##      3       42.0566             nan     0.1000    5.1609
##      4       37.6932             nan     0.1000    4.1360
##      5       33.9424             nan     0.1000    3.7307
##      6       30.0233             nan     0.1000    3.1631
##      7       27.3552             nan     0.1000    2.6061
##      8       24.9600             nan     0.1000    1.9477
##      9       22.6653             nan     0.1000    1.9668
##     10       20.9076             nan     0.1000    1.4590
##     20       11.3000             nan     0.1000    0.2648
##     40        6.1307             nan     0.1000    0.0326
##     60        5.0579             nan     0.1000    0.0327
##     80        4.7267             nan     0.1000   -0.0030
##    100        4.4504             nan     0.1000   -0.0164
##    120        4.3004             nan     0.1000   -0.0130
##    140        4.1323             nan     0.1000   -0.0222
##    160        4.0080             nan     0.1000   -0.0376
##    180        3.9045             nan     0.1000   -0.0196
##    200        3.8153             nan     0.1000   -0.0240
##    220        3.7631             nan     0.1000   -0.0191
##    240        3.6920             nan     0.1000   -0.0248
##    260        3.6260             nan     0.1000   -0.0014
##    280        3.5905             nan     0.1000   -0.0302
##    300        3.5211             nan     0.1000   -0.0181
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.7248             nan     0.1000    9.2858
##      2       43.2063             nan     0.1000    7.5471
##      3       36.5006             nan     0.1000    5.7241
##      4       31.2351             nan     0.1000    4.8840
##      5       26.7152             nan     0.1000    4.2072
##      6       23.0201             nan     0.1000    3.4714
##      7       20.1231             nan     0.1000    2.4880
##      8       17.5469             nan     0.1000    2.6343
##      9       15.6246             nan     0.1000    1.5836
##     10       13.8156             nan     0.1000    1.7091
##     20        5.7140             nan     0.1000    0.3476
##     40        3.4255             nan     0.1000   -0.0247
##     60        2.8808             nan     0.1000   -0.0209
##     80        2.5487             nan     0.1000   -0.0336
##    100        2.2685             nan     0.1000   -0.0374
##    120        2.0651             nan     0.1000   -0.0084
##    140        1.8614             nan     0.1000   -0.0207
##    160        1.7001             nan     0.1000   -0.0309
##    180        1.5659             nan     0.1000   -0.0292
##    200        1.4643             nan     0.1000   -0.0334
##    220        1.3380             nan     0.1000   -0.0175
##    240        1.2432             nan     0.1000   -0.0182
##    260        1.1622             nan     0.1000   -0.0081
##    280        1.1045             nan     0.1000   -0.0181
##    300        1.0439             nan     0.1000   -0.0124
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.4646             nan     0.1000    8.9282
##      2       42.9349             nan     0.1000    7.7266
##      3       36.5747             nan     0.1000    6.5063
##      4       31.2569             nan     0.1000    5.3462
##      5       27.1363             nan     0.1000    4.5185
##      6       23.5290             nan     0.1000    3.6736
##      7       20.5851             nan     0.1000    2.6638
##      8       18.0168             nan     0.1000    1.9615
##      9       15.7701             nan     0.1000    2.1022
##     10       13.9885             nan     0.1000    1.7248
##     20        5.9226             nan     0.1000    0.2677
##     40        3.6319             nan     0.1000    0.0126
##     60        3.0749             nan     0.1000   -0.0128
##     80        2.7207             nan     0.1000   -0.0233
##    100        2.5197             nan     0.1000   -0.0504
##    120        2.3332             nan     0.1000   -0.0350
##    140        2.1537             nan     0.1000   -0.0299
##    160        2.0318             nan     0.1000   -0.0123
##    180        1.9086             nan     0.1000   -0.0406
##    200        1.8073             nan     0.1000   -0.0197
##    220        1.7195             nan     0.1000   -0.0087
##    240        1.6083             nan     0.1000   -0.0175
##    260        1.5153             nan     0.1000   -0.0226
##    280        1.4391             nan     0.1000   -0.0261
##    300        1.3732             nan     0.1000   -0.0259
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.4166             nan     0.1000    8.9886
##      2       42.9857             nan     0.1000    7.5408
##      3       36.7442             nan     0.1000    5.7071
##      4       31.5188             nan     0.1000    5.3673
##      5       27.2102             nan     0.1000    3.8222
##      6       23.7300             nan     0.1000    3.4089
##      7       20.6408             nan     0.1000    2.8187
##      8       18.1909             nan     0.1000    2.4432
##      9       16.0387             nan     0.1000    2.3327
##     10       14.2283             nan     0.1000    1.6718
##     20        6.0296             nan     0.1000    0.2584
##     40        3.8770             nan     0.1000   -0.0702
##     60        3.3455             nan     0.1000   -0.0235
##     80        3.0897             nan     0.1000   -0.0296
##    100        2.8656             nan     0.1000   -0.0167
##    120        2.6807             nan     0.1000   -0.0168
##    140        2.5298             nan     0.1000   -0.0186
##    160        2.4246             nan     0.1000   -0.0132
##    180        2.2800             nan     0.1000   -0.0371
##    200        2.1757             nan     0.1000   -0.0113
##    220        2.0629             nan     0.1000   -0.0145
##    240        1.9731             nan     0.1000   -0.0129
##    260        1.8797             nan     0.1000   -0.0194
##    280        1.7918             nan     0.1000   -0.0259
##    300        1.7220             nan     0.1000   -0.0282
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.0360             nan     0.1000    9.5484
##      2       41.8452             nan     0.1000    7.7112
##      3       35.1514             nan     0.1000    6.1821
##      4       29.7073             nan     0.1000    4.9340
##      5       25.0406             nan     0.1000    4.3463
##      6       21.0266             nan     0.1000    3.5775
##      7       17.9713             nan     0.1000    2.7707
##      8       15.7020             nan     0.1000    2.0902
##      9       13.5466             nan     0.1000    2.1521
##     10       11.8641             nan     0.1000    1.6025
##     20        4.5786             nan     0.1000    0.1816
##     40        2.6642             nan     0.1000   -0.0197
##     60        2.1329             nan     0.1000   -0.0342
##     80        1.7763             nan     0.1000   -0.0151
##    100        1.4969             nan     0.1000   -0.0152
##    120        1.2975             nan     0.1000   -0.0166
##    140        1.1381             nan     0.1000   -0.0213
##    160        0.9880             nan     0.1000   -0.0128
##    180        0.8813             nan     0.1000   -0.0133
##    200        0.7783             nan     0.1000   -0.0059
##    220        0.6910             nan     0.1000   -0.0115
##    240        0.6224             nan     0.1000   -0.0082
##    260        0.5610             nan     0.1000   -0.0076
##    280        0.5034             nan     0.1000   -0.0080
##    300        0.4527             nan     0.1000   -0.0074
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.2778             nan     0.1000    8.9345
##      2       41.7666             nan     0.1000    7.7506
##      3       34.9156             nan     0.1000    6.8147
##      4       29.5401             nan     0.1000    5.6076
##      5       25.0086             nan     0.1000    4.5469
##      6       21.6057             nan     0.1000    3.8313
##      7       18.7128             nan     0.1000    3.0627
##      8       16.1744             nan     0.1000    2.2358
##      9       14.0793             nan     0.1000    2.0603
##     10       12.4610             nan     0.1000    1.1170
##     20        4.8868             nan     0.1000    0.2328
##     40        3.0229             nan     0.1000   -0.0134
##     60        2.5680             nan     0.1000   -0.0368
##     80        2.2054             nan     0.1000   -0.0141
##    100        1.9311             nan     0.1000   -0.0270
##    120        1.7509             nan     0.1000   -0.0236
##    140        1.6078             nan     0.1000   -0.0137
##    160        1.4672             nan     0.1000   -0.0156
##    180        1.3258             nan     0.1000   -0.0185
##    200        1.2247             nan     0.1000   -0.0197
##    220        1.1190             nan     0.1000   -0.0224
##    240        1.0206             nan     0.1000   -0.0170
##    260        0.9396             nan     0.1000   -0.0046
##    280        0.8615             nan     0.1000   -0.0167
##    300        0.7981             nan     0.1000   -0.0155
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.4441             nan     0.1000    8.8635
##      2       41.9766             nan     0.1000    7.6464
##      3       35.3747             nan     0.1000    6.3424
##      4       29.7385             nan     0.1000    5.3254
##      5       25.3737             nan     0.1000    4.7341
##      6       21.7400             nan     0.1000    3.3840
##      7       18.6101             nan     0.1000    2.9630
##      8       16.2892             nan     0.1000    2.2019
##      9       14.0839             nan     0.1000    2.0074
##     10       12.3625             nan     0.1000    1.5485
##     20        5.4973             nan     0.1000    0.1885
##     40        3.5820             nan     0.1000   -0.0063
##     60        3.1146             nan     0.1000   -0.0166
##     80        2.7698             nan     0.1000   -0.0601
##    100        2.4958             nan     0.1000   -0.0410
##    120        2.2761             nan     0.1000   -0.0009
##    140        2.0761             nan     0.1000   -0.0264
##    160        1.9385             nan     0.1000   -0.0237
##    180        1.7833             nan     0.1000   -0.0195
##    200        1.6533             nan     0.1000   -0.0340
##    220        1.5265             nan     0.1000   -0.0185
##    240        1.4255             nan     0.1000   -0.0151
##    260        1.3589             nan     0.1000   -0.0171
##    280        1.2813             nan     0.1000   -0.0090
##    300        1.2163             nan     0.1000   -0.0220
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.2471             nan     0.0100    0.8171
##      2       60.4397             nan     0.0100    0.7450
##      3       59.7327             nan     0.0100    0.6858
##      4       59.0314             nan     0.0100    0.7008
##      5       58.3337             nan     0.0100    0.7113
##      6       57.6094             nan     0.0100    0.7210
##      7       56.9120             nan     0.0100    0.6757
##      8       56.2622             nan     0.0100    0.7056
##      9       55.5959             nan     0.0100    0.6775
##     10       54.9864             nan     0.0100    0.6378
##     20       48.8743             nan     0.0100    0.5066
##     40       39.3633             nan     0.0100    0.3880
##     60       32.1468             nan     0.0100    0.3041
##     80       26.8350             nan     0.0100    0.2220
##    100       22.6433             nan     0.0100    0.1638
##    120       19.5177             nan     0.0100    0.1366
##    140       16.9959             nan     0.0100    0.1089
##    160       14.9314             nan     0.0100    0.0922
##    180       13.3013             nan     0.0100    0.0675
##    200       11.8810             nan     0.0100    0.0592
##    220       10.7510             nan     0.0100    0.0419
##    240        9.8464             nan     0.0100    0.0284
##    260        9.0426             nan     0.0100    0.0322
##    280        8.3754             nan     0.0100    0.0153
##    300        7.8088             nan     0.0100    0.0213
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.2335             nan     0.0100    0.7930
##      2       60.4126             nan     0.0100    0.7724
##      3       59.7121             nan     0.0100    0.7442
##      4       58.9596             nan     0.0100    0.7410
##      5       58.3147             nan     0.0100    0.6368
##      6       57.6453             nan     0.0100    0.7391
##      7       56.9562             nan     0.0100    0.6340
##      8       56.2538             nan     0.0100    0.6613
##      9       55.6347             nan     0.0100    0.6316
##     10       54.9292             nan     0.0100    0.6321
##     20       48.9372             nan     0.0100    0.5727
##     40       39.3745             nan     0.0100    0.3718
##     60       32.2644             nan     0.0100    0.3136
##     80       27.0154             nan     0.0100    0.2265
##    100       22.8407             nan     0.0100    0.1654
##    120       19.6301             nan     0.0100    0.1356
##    140       17.1105             nan     0.0100    0.1159
##    160       15.1062             nan     0.0100    0.0714
##    180       13.4058             nan     0.0100    0.0683
##    200       12.0099             nan     0.0100    0.0494
##    220       10.8768             nan     0.0100    0.0423
##    240        9.9340             nan     0.0100    0.0393
##    260        9.1577             nan     0.0100    0.0235
##    280        8.4650             nan     0.0100    0.0267
##    300        7.8962             nan     0.0100    0.0230
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.2664             nan     0.0100    0.7512
##      2       60.4755             nan     0.0100    0.7611
##      3       59.7056             nan     0.0100    0.7099
##      4       58.9449             nan     0.0100    0.7729
##      5       58.1742             nan     0.0100    0.7191
##      6       57.4567             nan     0.0100    0.6564
##      7       56.7102             nan     0.0100    0.6633
##      8       56.0161             nan     0.0100    0.6412
##      9       55.3484             nan     0.0100    0.6663
##     10       54.7126             nan     0.0100    0.6485
##     20       48.7478             nan     0.0100    0.5185
##     40       39.5092             nan     0.0100    0.3679
##     60       32.4741             nan     0.0100    0.3224
##     80       26.9540             nan     0.0100    0.1804
##    100       22.8327             nan     0.0100    0.1534
##    120       19.5640             nan     0.0100    0.1331
##    140       17.0115             nan     0.0100    0.0998
##    160       15.0070             nan     0.0100    0.0783
##    180       13.3386             nan     0.0100    0.0634
##    200       12.0034             nan     0.0100    0.0506
##    220       10.8655             nan     0.0100    0.0446
##    240        9.9849             nan     0.0100    0.0375
##    260        9.1796             nan     0.0100    0.0233
##    280        8.5283             nan     0.0100    0.0246
##    300        7.9556             nan     0.0100    0.0140
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.0756             nan     0.0100    1.0436
##      2       60.1121             nan     0.0100    0.9241
##      3       59.1398             nan     0.0100    0.8767
##      4       58.2601             nan     0.0100    0.9505
##      5       57.2728             nan     0.0100    0.8786
##      6       56.3825             nan     0.0100    0.8443
##      7       55.4643             nan     0.0100    0.9856
##      8       54.5975             nan     0.0100    0.9066
##      9       53.7411             nan     0.0100    0.9248
##     10       52.9454             nan     0.0100    0.8292
##     20       45.2351             nan     0.0100    0.6602
##     40       33.4845             nan     0.0100    0.4471
##     60       25.2464             nan     0.0100    0.3424
##     80       19.4439             nan     0.0100    0.2407
##    100       15.3330             nan     0.0100    0.1572
##    120       12.3388             nan     0.0100    0.0974
##    140       10.1592             nan     0.0100    0.0789
##    160        8.5966             nan     0.0100    0.0510
##    180        7.4095             nan     0.0100    0.0269
##    200        6.5071             nan     0.0100    0.0290
##    220        5.8273             nan     0.0100    0.0229
##    240        5.3414             nan     0.0100    0.0138
##    260        4.9522             nan     0.0100    0.0146
##    280        4.6608             nan     0.0100    0.0052
##    300        4.4169             nan     0.0100    0.0032
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.0249             nan     0.0100    1.0083
##      2       60.0501             nan     0.0100    0.8580
##      3       59.0255             nan     0.0100    1.0498
##      4       58.0567             nan     0.0100    0.8689
##      5       57.1328             nan     0.0100    0.9212
##      6       56.2389             nan     0.0100    0.9170
##      7       55.3481             nan     0.0100    0.8473
##      8       54.4487             nan     0.0100    0.8020
##      9       53.5667             nan     0.0100    0.7988
##     10       52.7073             nan     0.0100    0.7873
##     20       45.0638             nan     0.0100    0.6397
##     40       33.1983             nan     0.0100    0.4709
##     60       25.0138             nan     0.0100    0.3496
##     80       19.3344             nan     0.0100    0.2313
##    100       15.2786             nan     0.0100    0.1473
##    120       12.3923             nan     0.0100    0.1116
##    140       10.2626             nan     0.0100    0.0945
##    160        8.6657             nan     0.0100    0.0637
##    180        7.4821             nan     0.0100    0.0394
##    200        6.5980             nan     0.0100    0.0290
##    220        5.9308             nan     0.0100    0.0250
##    240        5.4515             nan     0.0100    0.0131
##    260        5.0887             nan     0.0100    0.0133
##    280        4.8044             nan     0.0100    0.0117
##    300        4.5858             nan     0.0100    0.0042
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.0146             nan     0.0100    1.1238
##      2       60.0157             nan     0.0100    1.1308
##      3       59.0649             nan     0.0100    1.0358
##      4       58.0879             nan     0.0100    0.8187
##      5       57.2114             nan     0.0100    0.8622
##      6       56.2606             nan     0.0100    0.9823
##      7       55.3185             nan     0.0100    0.8737
##      8       54.4709             nan     0.0100    0.8420
##      9       53.5584             nan     0.0100    0.8816
##     10       52.7162             nan     0.0100    0.8105
##     20       45.0614             nan     0.0100    0.6640
##     40       33.2360             nan     0.0100    0.4679
##     60       25.1540             nan     0.0100    0.3489
##     80       19.4696             nan     0.0100    0.2371
##    100       15.3101             nan     0.0100    0.1908
##    120       12.4223             nan     0.0100    0.1178
##    140       10.3664             nan     0.0100    0.0680
##    160        8.8373             nan     0.0100    0.0644
##    180        7.6617             nan     0.0100    0.0468
##    200        6.8181             nan     0.0100    0.0314
##    220        6.1964             nan     0.0100    0.0243
##    240        5.6959             nan     0.0100    0.0157
##    260        5.3067             nan     0.0100    0.0114
##    280        5.0145             nan     0.0100    0.0126
##    300        4.8007             nan     0.0100    0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.0165             nan     0.0100    1.0477
##      2       59.9716             nan     0.0100    1.0479
##      3       58.9321             nan     0.0100    1.0314
##      4       57.9297             nan     0.0100    1.0420
##      5       56.8560             nan     0.0100    1.0030
##      6       55.8343             nan     0.0100    1.1082
##      7       54.9082             nan     0.0100    0.9856
##      8       53.9656             nan     0.0100    0.8256
##      9       53.0861             nan     0.0100    0.8650
##     10       52.1747             nan     0.0100    0.8640
##     20       44.1799             nan     0.0100    0.7290
##     40       31.8072             nan     0.0100    0.5093
##     60       23.4430             nan     0.0100    0.3150
##     80       17.5244             nan     0.0100    0.2309
##    100       13.4227             nan     0.0100    0.1642
##    120       10.5454             nan     0.0100    0.1007
##    140        8.5015             nan     0.0100    0.0850
##    160        7.0931             nan     0.0100    0.0511
##    180        6.0581             nan     0.0100    0.0393
##    200        5.3591             nan     0.0100    0.0243
##    220        4.8015             nan     0.0100    0.0149
##    240        4.4036             nan     0.0100    0.0094
##    260        4.0873             nan     0.0100    0.0070
##    280        3.8529             nan     0.0100   -0.0023
##    300        3.6627             nan     0.0100   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.0055             nan     0.0100    1.0640
##      2       59.9550             nan     0.0100    1.0846
##      3       58.9050             nan     0.0100    1.0082
##      4       57.8709             nan     0.0100    0.9601
##      5       56.8651             nan     0.0100    1.0693
##      6       55.8442             nan     0.0100    0.9167
##      7       54.8700             nan     0.0100    0.8096
##      8       53.9720             nan     0.0100    0.8210
##      9       53.0454             nan     0.0100    0.8885
##     10       52.1660             nan     0.0100    0.8323
##     20       44.0084             nan     0.0100    0.6535
##     40       31.7890             nan     0.0100    0.5025
##     60       23.3379             nan     0.0100    0.3161
##     80       17.6059             nan     0.0100    0.2064
##    100       13.5621             nan     0.0100    0.1660
##    120       10.6917             nan     0.0100    0.1144
##    140        8.7166             nan     0.0100    0.0837
##    160        7.3243             nan     0.0100    0.0543
##    180        6.3081             nan     0.0100    0.0295
##    200        5.5555             nan     0.0100    0.0215
##    220        5.0228             nan     0.0100    0.0170
##    240        4.6105             nan     0.0100    0.0076
##    260        4.3088             nan     0.0100    0.0008
##    280        4.0892             nan     0.0100    0.0038
##    300        3.9008             nan     0.0100   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.9951             nan     0.0100    0.9559
##      2       59.9393             nan     0.0100    0.9891
##      3       58.8988             nan     0.0100    0.9849
##      4       57.8277             nan     0.0100    1.1098
##      5       56.8630             nan     0.0100    1.0348
##      6       55.8554             nan     0.0100    0.9254
##      7       54.9125             nan     0.0100    0.9681
##      8       53.9700             nan     0.0100    0.7811
##      9       53.1000             nan     0.0100    0.8305
##     10       52.2293             nan     0.0100    0.8243
##     20       44.1124             nan     0.0100    0.7188
##     40       31.9684             nan     0.0100    0.4830
##     60       23.5360             nan     0.0100    0.3569
##     80       17.7766             nan     0.0100    0.2328
##    100       13.7659             nan     0.0100    0.1549
##    120       10.9368             nan     0.0100    0.1074
##    140        8.9749             nan     0.0100    0.0610
##    160        7.5791             nan     0.0100    0.0477
##    180        6.5736             nan     0.0100    0.0359
##    200        5.8515             nan     0.0100    0.0259
##    220        5.3452             nan     0.0100    0.0110
##    240        4.9590             nan     0.0100    0.0148
##    260        4.6770             nan     0.0100    0.0030
##    280        4.4678             nan     0.0100    0.0046
##    300        4.2982             nan     0.0100    0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.2510             nan     0.0500    3.8659
##      2       54.9483             nan     0.0500    3.2659
##      3       51.7792             nan     0.0500    3.0892
##      4       48.8502             nan     0.0500    2.9909
##      5       46.2271             nan     0.0500    2.5995
##      6       43.8243             nan     0.0500    2.3293
##      7       41.3860             nan     0.0500    2.3403
##      8       39.3449             nan     0.0500    2.0760
##      9       37.2151             nan     0.0500    2.0170
##     10       35.4728             nan     0.0500    1.5757
##     20       23.2106             nan     0.0500    0.8434
##     40       12.2243             nan     0.0500    0.2998
##     60        7.9661             nan     0.0500    0.1322
##     80        6.0688             nan     0.0500   -0.0020
##    100        5.1843             nan     0.0500    0.0107
##    120        4.7871             nan     0.0500    0.0097
##    140        4.6039             nan     0.0500   -0.0185
##    160        4.4874             nan     0.0500   -0.0098
##    180        4.3956             nan     0.0500   -0.0054
##    200        4.3290             nan     0.0500   -0.0157
##    220        4.2400             nan     0.0500   -0.0015
##    240        4.1610             nan     0.0500   -0.0035
##    260        4.0935             nan     0.0500   -0.0111
##    280        4.0350             nan     0.0500   -0.0390
##    300        3.9945             nan     0.0500   -0.0038
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.3960             nan     0.0500    3.8379
##      2       54.9671             nan     0.0500    3.4623
##      3       51.4929             nan     0.0500    3.0140
##      4       48.6050             nan     0.0500    2.8483
##      5       45.9079             nan     0.0500    2.4888
##      6       43.7300             nan     0.0500    2.2019
##      7       41.0662             nan     0.0500    2.1655
##      8       38.7998             nan     0.0500    1.8705
##      9       36.9989             nan     0.0500    1.8070
##     10       35.3301             nan     0.0500    1.7297
##     20       22.1881             nan     0.0500    0.8367
##     40       11.8501             nan     0.0500    0.1725
##     60        7.7668             nan     0.0500    0.1199
##     80        6.0438             nan     0.0500    0.0432
##    100        5.2471             nan     0.0500   -0.0102
##    120        4.8393             nan     0.0500   -0.0134
##    140        4.6629             nan     0.0500   -0.0027
##    160        4.5407             nan     0.0500   -0.0091
##    180        4.4433             nan     0.0500   -0.0095
##    200        4.3598             nan     0.0500   -0.0017
##    220        4.3072             nan     0.0500    0.0003
##    240        4.2494             nan     0.0500   -0.0223
##    260        4.1912             nan     0.0500   -0.0101
##    280        4.1356             nan     0.0500   -0.0086
##    300        4.0780             nan     0.0500   -0.0042
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.0784             nan     0.0500    4.1658
##      2       54.5911             nan     0.0500    3.2199
##      3       51.4127             nan     0.0500    3.0460
##      4       48.4753             nan     0.0500    2.9579
##      5       45.7399             nan     0.0500    2.4592
##      6       43.2509             nan     0.0500    2.4544
##      7       41.0372             nan     0.0500    2.1524
##      8       39.1829             nan     0.0500    1.9616
##      9       37.3444             nan     0.0500    1.7148
##     10       35.6296             nan     0.0500    1.7590
##     20       22.9508             nan     0.0500    0.5356
##     40       11.7975             nan     0.0500    0.2155
##     60        8.0006             nan     0.0500    0.0976
##     80        6.1805             nan     0.0500    0.0126
##    100        5.3689             nan     0.0500   -0.0095
##    120        5.0349             nan     0.0500   -0.0294
##    140        4.8268             nan     0.0500   -0.0081
##    160        4.7045             nan     0.0500   -0.0124
##    180        4.6158             nan     0.0500   -0.0149
##    200        4.5387             nan     0.0500   -0.0047
##    220        4.4880             nan     0.0500   -0.0078
##    240        4.4157             nan     0.0500   -0.0124
##    260        4.3487             nan     0.0500   -0.0098
##    280        4.3108             nan     0.0500   -0.0146
##    300        4.2531             nan     0.0500   -0.0117
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.0509             nan     0.0500    4.9164
##      2       52.6697             nan     0.0500    4.1244
##      3       48.5121             nan     0.0500    3.7657
##      4       44.9575             nan     0.0500    3.6333
##      5       41.7553             nan     0.0500    3.3531
##      6       38.5213             nan     0.0500    3.1923
##      7       35.5831             nan     0.0500    3.0295
##      8       33.0286             nan     0.0500    2.5774
##      9       30.6986             nan     0.0500    2.1684
##     10       28.7020             nan     0.0500    2.1467
##     20       15.1303             nan     0.0500    0.8677
##     40        6.5465             nan     0.0500    0.1700
##     60        4.4643             nan     0.0500    0.0398
##     80        3.7855             nan     0.0500   -0.0092
##    100        3.3733             nan     0.0500   -0.0265
##    120        3.1284             nan     0.0500   -0.0170
##    140        2.9125             nan     0.0500   -0.0167
##    160        2.7485             nan     0.0500   -0.0306
##    180        2.5813             nan     0.0500   -0.0099
##    200        2.4451             nan     0.0500   -0.0085
##    220        2.3395             nan     0.0500   -0.0169
##    240        2.2064             nan     0.0500   -0.0071
##    260        2.1134             nan     0.0500   -0.0148
##    280        2.0193             nan     0.0500   -0.0121
##    300        1.9274             nan     0.0500   -0.0259
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.9425             nan     0.0500    5.1620
##      2       52.3220             nan     0.0500    4.1111
##      3       48.0163             nan     0.0500    3.8190
##      4       44.2704             nan     0.0500    3.5705
##      5       40.9959             nan     0.0500    3.4008
##      6       37.8627             nan     0.0500    2.8173
##      7       35.1591             nan     0.0500    2.6522
##      8       32.5420             nan     0.0500    2.3474
##      9       30.0371             nan     0.0500    2.2974
##     10       27.8753             nan     0.0500    1.7058
##     20       14.6966             nan     0.0500    0.8123
##     40        6.5102             nan     0.0500    0.1154
##     60        4.5803             nan     0.0500    0.0179
##     80        3.9867             nan     0.0500   -0.0312
##    100        3.6925             nan     0.0500   -0.0373
##    120        3.4431             nan     0.0500   -0.0100
##    140        3.2715             nan     0.0500   -0.0312
##    160        3.1237             nan     0.0500   -0.0117
##    180        2.9960             nan     0.0500   -0.0188
##    200        2.9043             nan     0.0500   -0.0278
##    220        2.7744             nan     0.0500   -0.0157
##    240        2.6667             nan     0.0500   -0.0303
##    260        2.5674             nan     0.0500   -0.0095
##    280        2.4871             nan     0.0500   -0.0188
##    300        2.3901             nan     0.0500   -0.0260
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.0138             nan     0.0500    4.8806
##      2       52.4046             nan     0.0500    4.3052
##      3       48.1771             nan     0.0500    4.0895
##      4       44.6355             nan     0.0500    3.7533
##      5       41.2741             nan     0.0500    2.9681
##      6       38.2607             nan     0.0500    2.8724
##      7       35.4450             nan     0.0500    2.7316
##      8       32.8851             nan     0.0500    2.1201
##      9       30.5620             nan     0.0500    2.0669
##     10       28.5482             nan     0.0500    1.9160
##     20       15.4266             nan     0.0500    0.7355
##     40        6.7283             nan     0.0500    0.1622
##     60        4.7553             nan     0.0500   -0.0083
##     80        4.1564             nan     0.0500   -0.0050
##    100        3.8614             nan     0.0500   -0.0238
##    120        3.6413             nan     0.0500   -0.0073
##    140        3.5058             nan     0.0500   -0.0266
##    160        3.3426             nan     0.0500   -0.0188
##    180        3.2226             nan     0.0500   -0.0181
##    200        3.1103             nan     0.0500   -0.0129
##    220        3.0073             nan     0.0500   -0.0249
##    240        2.9350             nan     0.0500   -0.0097
##    260        2.8251             nan     0.0500   -0.0103
##    280        2.7574             nan     0.0500   -0.0075
##    300        2.6761             nan     0.0500   -0.0163
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.8288             nan     0.0500    5.2259
##      2       52.1941             nan     0.0500    4.6416
##      3       48.1045             nan     0.0500    4.0980
##      4       44.0999             nan     0.0500    3.3660
##      5       40.4685             nan     0.0500    3.2812
##      6       37.2454             nan     0.0500    3.6083
##      7       34.1335             nan     0.0500    2.8631
##      8       31.5240             nan     0.0500    2.4587
##      9       29.1742             nan     0.0500    2.1428
##     10       26.8730             nan     0.0500    2.1001
##     20       13.4004             nan     0.0500    0.8190
##     40        5.2974             nan     0.0500    0.1081
##     60        3.6409             nan     0.0500   -0.0084
##     80        3.0458             nan     0.0500   -0.0107
##    100        2.7261             nan     0.0500   -0.0317
##    120        2.4446             nan     0.0500   -0.0325
##    140        2.2115             nan     0.0500   -0.0162
##    160        1.9837             nan     0.0500   -0.0142
##    180        1.8272             nan     0.0500   -0.0178
##    200        1.6830             nan     0.0500   -0.0170
##    220        1.5669             nan     0.0500   -0.0166
##    240        1.4746             nan     0.0500   -0.0131
##    260        1.3603             nan     0.0500   -0.0207
##    280        1.2661             nan     0.0500   -0.0174
##    300        1.1680             nan     0.0500   -0.0056
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.8123             nan     0.0500    5.3557
##      2       51.8963             nan     0.0500    4.3317
##      3       47.6316             nan     0.0500    4.3542
##      4       43.8881             nan     0.0500    3.9939
##      5       40.4536             nan     0.0500    3.7371
##      6       37.3856             nan     0.0500    2.9395
##      7       34.5803             nan     0.0500    3.0939
##      8       31.7217             nan     0.0500    2.3956
##      9       29.3008             nan     0.0500    2.3267
##     10       27.2113             nan     0.0500    2.0625
##     20       13.3472             nan     0.0500    0.8152
##     40        5.4916             nan     0.0500    0.1057
##     60        3.9257             nan     0.0500    0.0078
##     80        3.3900             nan     0.0500   -0.0255
##    100        3.0654             nan     0.0500   -0.0153
##    120        2.7759             nan     0.0500   -0.0105
##    140        2.6206             nan     0.0500   -0.0235
##    160        2.4814             nan     0.0500   -0.0235
##    180        2.3341             nan     0.0500   -0.0282
##    200        2.1656             nan     0.0500   -0.0143
##    220        2.0448             nan     0.0500   -0.0162
##    240        1.9491             nan     0.0500   -0.0229
##    260        1.8512             nan     0.0500   -0.0216
##    280        1.7562             nan     0.0500   -0.0140
##    300        1.6646             nan     0.0500   -0.0048
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.7805             nan     0.0500    5.1416
##      2       52.0776             nan     0.0500    4.5647
##      3       47.6773             nan     0.0500    4.3866
##      4       43.7995             nan     0.0500    4.0749
##      5       40.1579             nan     0.0500    3.3395
##      6       36.9355             nan     0.0500    3.0672
##      7       33.9534             nan     0.0500    2.8057
##      8       31.3132             nan     0.0500    2.2860
##      9       28.8416             nan     0.0500    2.1411
##     10       26.7253             nan     0.0500    1.8991
##     20       13.3205             nan     0.0500    0.7813
##     40        5.7091             nan     0.0500    0.1037
##     60        4.2640             nan     0.0500   -0.0049
##     80        3.8119             nan     0.0500   -0.0376
##    100        3.5128             nan     0.0500   -0.0240
##    120        3.3067             nan     0.0500   -0.0220
##    140        3.1178             nan     0.0500   -0.0032
##    160        2.9730             nan     0.0500   -0.0221
##    180        2.8217             nan     0.0500   -0.0100
##    200        2.6794             nan     0.0500   -0.0087
##    220        2.5736             nan     0.0500   -0.0121
##    240        2.4393             nan     0.0500   -0.0119
##    260        2.3314             nan     0.0500   -0.0181
##    280        2.2384             nan     0.0500   -0.0124
##    300        2.1517             nan     0.0500   -0.0140
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.9715             nan     0.1000    7.0198
##      2       48.7284             nan     0.1000    6.0682
##      3       43.0850             nan     0.1000    4.6082
##      4       38.6990             nan     0.1000    4.1440
##      5       34.9746             nan     0.1000    3.3631
##      6       31.3431             nan     0.1000    3.2554
##      7       28.5754             nan     0.1000    2.5183
##      8       25.8712             nan     0.1000    2.0595
##      9       24.1180             nan     0.1000    1.6862
##     10       22.5115             nan     0.1000    1.5865
##     20       11.7230             nan     0.1000    0.4389
##     40        6.0035             nan     0.1000   -0.0010
##     60        4.8913             nan     0.1000    0.0024
##     80        4.6013             nan     0.1000   -0.0361
##    100        4.4268             nan     0.1000   -0.0313
##    120        4.2823             nan     0.1000   -0.0175
##    140        4.1785             nan     0.1000   -0.0100
##    160        4.0911             nan     0.1000   -0.0423
##    180        4.0082             nan     0.1000   -0.0567
##    200        3.9056             nan     0.1000   -0.0120
##    220        3.8073             nan     0.1000   -0.0044
##    240        3.7222             nan     0.1000   -0.0130
##    260        3.6461             nan     0.1000   -0.0072
##    280        3.5788             nan     0.1000   -0.0369
##    300        3.5061             nan     0.1000   -0.0205
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.4538             nan     0.1000    7.4325
##      2       48.5028             nan     0.1000    6.1401
##      3       43.8059             nan     0.1000    4.9498
##      4       39.0279             nan     0.1000    4.3574
##      5       35.4411             nan     0.1000    3.8483
##      6       32.2688             nan     0.1000    3.2946
##      7       29.3795             nan     0.1000    2.8381
##      8       27.0367             nan     0.1000    1.9143
##      9       24.5248             nan     0.1000    2.0962
##     10       22.5315             nan     0.1000    1.9347
##     20       11.8805             nan     0.1000    0.6654
##     40        6.0573             nan     0.1000    0.0025
##     60        4.9301             nan     0.1000   -0.0363
##     80        4.6375             nan     0.1000    0.0014
##    100        4.4643             nan     0.1000   -0.0295
##    120        4.3344             nan     0.1000   -0.0318
##    140        4.2078             nan     0.1000   -0.0197
##    160        4.0812             nan     0.1000   -0.0163
##    180        3.9932             nan     0.1000   -0.0458
##    200        3.8917             nan     0.1000   -0.0093
##    220        3.8157             nan     0.1000   -0.0315
##    240        3.7674             nan     0.1000   -0.0250
##    260        3.6985             nan     0.1000   -0.0056
##    280        3.6336             nan     0.1000    0.0005
##    300        3.5467             nan     0.1000   -0.0326
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.8076             nan     0.1000    7.1677
##      2       49.1246             nan     0.1000    5.8667
##      3       44.3561             nan     0.1000    4.3528
##      4       39.8620             nan     0.1000    4.4319
##      5       35.5466             nan     0.1000    4.4480
##      6       32.0148             nan     0.1000    2.8939
##      7       29.2254             nan     0.1000    2.6131
##      8       26.4858             nan     0.1000    2.3813
##      9       24.1384             nan     0.1000    2.1010
##     10       22.1882             nan     0.1000    1.9119
##     20       11.7862             nan     0.1000    0.5451
##     40        6.1845             nan     0.1000    0.0485
##     60        5.1008             nan     0.1000   -0.0325
##     80        4.8234             nan     0.1000    0.0120
##    100        4.5990             nan     0.1000   -0.0033
##    120        4.4453             nan     0.1000   -0.0131
##    140        4.3541             nan     0.1000   -0.0176
##    160        4.2599             nan     0.1000   -0.0030
##    180        4.1840             nan     0.1000   -0.0513
##    200        4.0914             nan     0.1000   -0.0424
##    220        4.0280             nan     0.1000   -0.0460
##    240        3.9639             nan     0.1000   -0.0059
##    260        3.8893             nan     0.1000   -0.0062
##    280        3.8233             nan     0.1000   -0.0216
##    300        3.7812             nan     0.1000   -0.0077
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.4696             nan     0.1000   10.0387
##      2       44.0876             nan     0.1000    8.4219
##      3       37.4773             nan     0.1000    5.6257
##      4       32.0671             nan     0.1000    4.8524
##      5       27.5576             nan     0.1000    3.3977
##      6       23.7786             nan     0.1000    3.5954
##      7       20.7378             nan     0.1000    3.0122
##      8       18.2250             nan     0.1000    2.3834
##      9       16.2712             nan     0.1000    1.8746
##     10       14.3526             nan     0.1000    1.6939
##     20        6.2810             nan     0.1000    0.3110
##     40        3.9163             nan     0.1000   -0.0380
##     60        3.2436             nan     0.1000   -0.0344
##     80        2.8314             nan     0.1000   -0.0247
##    100        2.5282             nan     0.1000   -0.0115
##    120        2.2960             nan     0.1000   -0.0135
##    140        2.0580             nan     0.1000   -0.0416
##    160        1.8536             nan     0.1000   -0.0102
##    180        1.7043             nan     0.1000   -0.0245
##    200        1.5893             nan     0.1000   -0.0380
##    220        1.4785             nan     0.1000   -0.0197
##    240        1.3549             nan     0.1000   -0.0197
##    260        1.2608             nan     0.1000   -0.0142
##    280        1.1749             nan     0.1000   -0.0086
##    300        1.1093             nan     0.1000   -0.0141
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.3949             nan     0.1000    9.6151
##      2       44.4557             nan     0.1000    7.8735
##      3       37.9398             nan     0.1000    6.1136
##      4       32.4552             nan     0.1000    5.5608
##      5       27.8174             nan     0.1000    4.0227
##      6       24.2423             nan     0.1000    3.7077
##      7       21.3314             nan     0.1000    2.9468
##      8       18.7740             nan     0.1000    2.2866
##      9       16.8861             nan     0.1000    2.0097
##     10       15.2661             nan     0.1000    1.6202
##     20        6.6689             nan     0.1000    0.1888
##     40        4.1151             nan     0.1000   -0.0398
##     60        3.5551             nan     0.1000   -0.0256
##     80        3.2555             nan     0.1000   -0.0548
##    100        2.9841             nan     0.1000   -0.0295
##    120        2.7619             nan     0.1000   -0.0303
##    140        2.5536             nan     0.1000   -0.0316
##    160        2.4049             nan     0.1000   -0.0079
##    180        2.2955             nan     0.1000   -0.0522
##    200        2.1531             nan     0.1000   -0.0189
##    220        2.0583             nan     0.1000   -0.0273
##    240        1.9377             nan     0.1000   -0.0362
##    260        1.8371             nan     0.1000   -0.0132
##    280        1.7450             nan     0.1000   -0.0195
##    300        1.6536             nan     0.1000   -0.0239
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.7991             nan     0.1000    9.0010
##      2       44.8187             nan     0.1000    8.0573
##      3       38.1452             nan     0.1000    7.1665
##      4       32.6997             nan     0.1000    5.5486
##      5       28.1284             nan     0.1000    4.4632
##      6       24.3199             nan     0.1000    3.3914
##      7       20.9843             nan     0.1000    3.1358
##      8       18.3428             nan     0.1000    2.4338
##      9       16.4125             nan     0.1000    2.0159
##     10       14.4656             nan     0.1000    1.4699
##     20        6.7510             nan     0.1000    0.3631
##     40        4.2672             nan     0.1000   -0.0018
##     60        3.7883             nan     0.1000   -0.0640
##     80        3.4441             nan     0.1000   -0.0236
##    100        3.2037             nan     0.1000   -0.0341
##    120        3.0127             nan     0.1000   -0.0426
##    140        2.7514             nan     0.1000   -0.0181
##    160        2.6241             nan     0.1000   -0.0175
##    180        2.4976             nan     0.1000   -0.0186
##    200        2.3551             nan     0.1000   -0.0225
##    220        2.2428             nan     0.1000   -0.0187
##    240        2.1528             nan     0.1000   -0.0362
##    260        2.0643             nan     0.1000   -0.0261
##    280        1.9576             nan     0.1000   -0.0155
##    300        1.8813             nan     0.1000   -0.0339
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.1119             nan     0.1000   10.1801
##      2       43.9556             nan     0.1000    8.0204
##      3       36.7805             nan     0.1000    6.9267
##      4       31.3349             nan     0.1000    5.4869
##      5       26.8050             nan     0.1000    4.8037
##      6       22.9288             nan     0.1000    3.3492
##      7       19.8097             nan     0.1000    3.2726
##      8       17.2449             nan     0.1000    2.4774
##      9       14.7408             nan     0.1000    2.1059
##     10       12.8890             nan     0.1000    1.6024
##     20        5.2312             nan     0.1000    0.1668
##     40        3.0627             nan     0.1000   -0.0497
##     60        2.4368             nan     0.1000   -0.0341
##     80        2.0174             nan     0.1000   -0.0186
##    100        1.7268             nan     0.1000   -0.0274
##    120        1.5123             nan     0.1000   -0.0335
##    140        1.3280             nan     0.1000   -0.0188
##    160        1.1530             nan     0.1000   -0.0235
##    180        1.0248             nan     0.1000   -0.0078
##    200        0.9313             nan     0.1000   -0.0153
##    220        0.8278             nan     0.1000   -0.0149
##    240        0.7259             nan     0.1000   -0.0149
##    260        0.6464             nan     0.1000   -0.0102
##    280        0.5767             nan     0.1000   -0.0119
##    300        0.5298             nan     0.1000   -0.0115
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.0656             nan     0.1000   10.1341
##      2       43.9084             nan     0.1000    7.9801
##      3       37.0762             nan     0.1000    7.0906
##      4       31.0623             nan     0.1000    5.5155
##      5       26.2915             nan     0.1000    3.9790
##      6       22.6846             nan     0.1000    3.2854
##      7       19.5807             nan     0.1000    2.8688
##      8       16.9771             nan     0.1000    2.5670
##      9       14.7056             nan     0.1000    2.1748
##     10       13.0735             nan     0.1000    1.6088
##     20        5.4294             nan     0.1000    0.2670
##     40        3.4078             nan     0.1000   -0.0356
##     60        2.9168             nan     0.1000   -0.0378
##     80        2.5150             nan     0.1000   -0.0234
##    100        2.2520             nan     0.1000   -0.0204
##    120        2.0312             nan     0.1000   -0.0364
##    140        1.7791             nan     0.1000   -0.0303
##    160        1.6362             nan     0.1000   -0.0350
##    180        1.4804             nan     0.1000   -0.0207
##    200        1.3572             nan     0.1000   -0.0084
##    220        1.2256             nan     0.1000   -0.0153
##    240        1.1251             nan     0.1000   -0.0110
##    260        1.0616             nan     0.1000   -0.0150
##    280        0.9769             nan     0.1000   -0.0152
##    300        0.9174             nan     0.1000   -0.0153
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.7891             nan     0.1000    9.8440
##      2       43.3241             nan     0.1000    7.7736
##      3       36.4685             nan     0.1000    6.4494
##      4       30.7599             nan     0.1000    5.5524
##      5       26.1759             nan     0.1000    4.5273
##      6       22.3005             nan     0.1000    3.4149
##      7       19.2184             nan     0.1000    2.9957
##      8       16.7347             nan     0.1000    2.5685
##      9       14.5609             nan     0.1000    1.7038
##     10       12.9032             nan     0.1000    1.6362
##     20        5.6709             nan     0.1000    0.2826
##     40        3.8271             nan     0.1000   -0.0562
##     60        3.3528             nan     0.1000   -0.0259
##     80        3.0576             nan     0.1000   -0.0262
##    100        2.7595             nan     0.1000   -0.0389
##    120        2.4658             nan     0.1000   -0.0333
##    140        2.2557             nan     0.1000   -0.0554
##    160        2.0816             nan     0.1000   -0.0224
##    180        1.9530             nan     0.1000   -0.0394
##    200        1.7937             nan     0.1000   -0.0564
##    220        1.6793             nan     0.1000   -0.0134
##    240        1.5793             nan     0.1000   -0.0152
##    260        1.4946             nan     0.1000   -0.0225
##    280        1.4172             nan     0.1000   -0.0181
##    300        1.3446             nan     0.1000   -0.0199
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.2761             nan     0.0100    0.8008
##      2       59.5575             nan     0.0100    0.7360
##      3       58.7857             nan     0.0100    0.7765
##      4       58.0223             nan     0.0100    0.6594
##      5       57.2902             nan     0.0100    0.7141
##      6       56.5739             nan     0.0100    0.7383
##      7       55.9285             nan     0.0100    0.6857
##      8       55.2359             nan     0.0100    0.6329
##      9       54.5971             nan     0.0100    0.6439
##     10       53.9664             nan     0.0100    0.6208
##     20       48.0228             nan     0.0100    0.5131
##     40       38.9565             nan     0.0100    0.3685
##     60       32.0781             nan     0.0100    0.2316
##     80       26.9190             nan     0.0100    0.1754
##    100       22.8628             nan     0.0100    0.1511
##    120       19.6574             nan     0.0100    0.1157
##    140       17.1367             nan     0.0100    0.1068
##    160       15.0963             nan     0.0100    0.0742
##    180       13.3989             nan     0.0100    0.0511
##    200       12.0526             nan     0.0100    0.0478
##    220       10.9251             nan     0.0100    0.0443
##    240        9.9798             nan     0.0100    0.0297
##    260        9.1773             nan     0.0100    0.0267
##    280        8.4882             nan     0.0100    0.0215
##    300        7.9452             nan     0.0100    0.0179
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.3358             nan     0.0100    0.7410
##      2       59.6381             nan     0.0100    0.7717
##      3       58.9181             nan     0.0100    0.7194
##      4       58.1778             nan     0.0100    0.7511
##      5       57.4200             nan     0.0100    0.6840
##      6       56.7205             nan     0.0100    0.6435
##      7       56.0325             nan     0.0100    0.6156
##      8       55.2890             nan     0.0100    0.6614
##      9       54.6191             nan     0.0100    0.5893
##     10       53.9630             nan     0.0100    0.6470
##     20       48.1116             nan     0.0100    0.5407
##     40       38.8595             nan     0.0100    0.3931
##     60       31.8734             nan     0.0100    0.2925
##     80       26.6940             nan     0.0100    0.2177
##    100       22.7128             nan     0.0100    0.1504
##    120       19.6294             nan     0.0100    0.1181
##    140       17.1552             nan     0.0100    0.1045
##    160       15.1093             nan     0.0100    0.0684
##    180       13.4372             nan     0.0100    0.0561
##    200       12.1154             nan     0.0100    0.0471
##    220       11.0314             nan     0.0100    0.0306
##    240       10.0815             nan     0.0100    0.0335
##    260        9.2700             nan     0.0100    0.0212
##    280        8.5830             nan     0.0100    0.0259
##    300        8.0039             nan     0.0100    0.0283
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.3345             nan     0.0100    0.7580
##      2       59.5236             nan     0.0100    0.7380
##      3       58.8020             nan     0.0100    0.7468
##      4       58.0023             nan     0.0100    0.7469
##      5       57.2910             nan     0.0100    0.7150
##      6       56.5618             nan     0.0100    0.6706
##      7       55.8565             nan     0.0100    0.6944
##      8       55.1813             nan     0.0100    0.6619
##      9       54.5096             nan     0.0100    0.6846
##     10       53.8259             nan     0.0100    0.6196
##     20       47.9957             nan     0.0100    0.4979
##     40       39.0757             nan     0.0100    0.3924
##     60       32.0510             nan     0.0100    0.2917
##     80       26.9588             nan     0.0100    0.2144
##    100       22.8597             nan     0.0100    0.1579
##    120       19.6858             nan     0.0100    0.1303
##    140       17.1854             nan     0.0100    0.0814
##    160       15.1114             nan     0.0100    0.0900
##    180       13.4924             nan     0.0100    0.0717
##    200       12.1109             nan     0.0100    0.0486
##    220       10.9870             nan     0.0100    0.0187
##    240       10.0550             nan     0.0100    0.0327
##    260        9.2751             nan     0.0100    0.0275
##    280        8.6431             nan     0.0100    0.0217
##    300        8.0886             nan     0.0100    0.0120
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.0433             nan     0.0100    0.9634
##      2       59.1019             nan     0.0100    0.9004
##      3       58.0928             nan     0.0100    0.8345
##      4       57.1744             nan     0.0100    0.8803
##      5       56.1992             nan     0.0100    1.0349
##      6       55.3106             nan     0.0100    0.9595
##      7       54.4148             nan     0.0100    0.9659
##      8       53.5565             nan     0.0100    0.8706
##      9       52.7349             nan     0.0100    0.8061
##     10       51.9506             nan     0.0100    0.7888
##     20       44.4523             nan     0.0100    0.7300
##     40       32.8263             nan     0.0100    0.4898
##     60       24.7771             nan     0.0100    0.3550
##     80       19.1013             nan     0.0100    0.1695
##    100       15.1295             nan     0.0100    0.1507
##    120       12.2295             nan     0.0100    0.1122
##    140       10.1323             nan     0.0100    0.0880
##    160        8.6122             nan     0.0100    0.0665
##    180        7.4402             nan     0.0100    0.0389
##    200        6.5393             nan     0.0100    0.0328
##    220        5.8724             nan     0.0100    0.0198
##    240        5.3834             nan     0.0100    0.0175
##    260        4.9905             nan     0.0100    0.0093
##    280        4.6939             nan     0.0100    0.0063
##    300        4.4521             nan     0.0100   -0.0038
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.0225             nan     0.0100    1.0045
##      2       58.9893             nan     0.0100    1.1062
##      3       58.0600             nan     0.0100    0.8951
##      4       57.1438             nan     0.0100    0.9146
##      5       56.1944             nan     0.0100    0.8960
##      6       55.3194             nan     0.0100    0.9240
##      7       54.4099             nan     0.0100    0.9043
##      8       53.4969             nan     0.0100    0.9374
##      9       52.5711             nan     0.0100    0.9185
##     10       51.7597             nan     0.0100    0.8255
##     20       44.2439             nan     0.0100    0.6944
##     40       32.7609             nan     0.0100    0.4770
##     60       24.6358             nan     0.0100    0.3200
##     80       18.9947             nan     0.0100    0.2315
##    100       15.0428             nan     0.0100    0.1512
##    120       12.2678             nan     0.0100    0.1038
##    140       10.1625             nan     0.0100    0.0823
##    160        8.6497             nan     0.0100    0.0484
##    180        7.5598             nan     0.0100    0.0451
##    200        6.7065             nan     0.0100    0.0309
##    220        6.0656             nan     0.0100    0.0145
##    240        5.5642             nan     0.0100    0.0142
##    260        5.1777             nan     0.0100    0.0095
##    280        4.8908             nan     0.0100    0.0092
##    300        4.6551             nan     0.0100    0.0076
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.0794             nan     0.0100    0.9119
##      2       59.0456             nan     0.0100    0.8568
##      3       58.0929             nan     0.0100    0.8442
##      4       57.1425             nan     0.0100    0.8541
##      5       56.1999             nan     0.0100    0.9438
##      6       55.2876             nan     0.0100    0.8320
##      7       54.3969             nan     0.0100    0.7467
##      8       53.5732             nan     0.0100    0.8576
##      9       52.7422             nan     0.0100    0.8188
##     10       51.8967             nan     0.0100    0.8492
##     20       44.3652             nan     0.0100    0.6170
##     40       32.7914             nan     0.0100    0.4969
##     60       24.7314             nan     0.0100    0.2972
##     80       19.1481             nan     0.0100    0.2199
##    100       15.2114             nan     0.0100    0.1497
##    120       12.3881             nan     0.0100    0.1018
##    140       10.3871             nan     0.0100    0.0864
##    160        8.8829             nan     0.0100    0.0540
##    180        7.7649             nan     0.0100    0.0396
##    200        6.9283             nan     0.0100    0.0306
##    220        6.3078             nan     0.0100    0.0191
##    240        5.8241             nan     0.0100    0.0129
##    260        5.4501             nan     0.0100    0.0027
##    280        5.1501             nan     0.0100    0.0063
##    300        4.9434             nan     0.0100   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.0306             nan     0.0100    1.0784
##      2       59.0280             nan     0.0100    1.0730
##      3       57.9801             nan     0.0100    1.0094
##      4       56.9847             nan     0.0100    1.0551
##      5       56.0450             nan     0.0100    1.0124
##      6       55.0641             nan     0.0100    1.0696
##      7       54.1206             nan     0.0100    0.9663
##      8       53.2228             nan     0.0100    0.9031
##      9       52.2910             nan     0.0100    0.9739
##     10       51.4144             nan     0.0100    0.9420
##     20       43.3036             nan     0.0100    0.6559
##     40       31.2040             nan     0.0100    0.4875
##     60       22.8807             nan     0.0100    0.3367
##     80       17.2145             nan     0.0100    0.2408
##    100       13.3191             nan     0.0100    0.1538
##    120       10.5060             nan     0.0100    0.1223
##    140        8.5624             nan     0.0100    0.0757
##    160        7.1432             nan     0.0100    0.0341
##    180        6.1330             nan     0.0100    0.0330
##    200        5.3965             nan     0.0100    0.0304
##    220        4.8386             nan     0.0100    0.0045
##    240        4.4328             nan     0.0100    0.0084
##    260        4.1189             nan     0.0100    0.0065
##    280        3.8600             nan     0.0100    0.0061
##    300        3.6612             nan     0.0100    0.0019
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.0031             nan     0.0100    1.1033
##      2       58.9591             nan     0.0100    1.0304
##      3       57.9819             nan     0.0100    0.9649
##      4       56.9504             nan     0.0100    0.9749
##      5       56.0079             nan     0.0100    0.8591
##      6       55.0450             nan     0.0100    0.9702
##      7       54.1063             nan     0.0100    0.8210
##      8       53.2218             nan     0.0100    0.8920
##      9       52.3569             nan     0.0100    0.8488
##     10       51.4483             nan     0.0100    0.9247
##     20       43.4312             nan     0.0100    0.7435
##     40       31.3662             nan     0.0100    0.4730
##     60       23.2167             nan     0.0100    0.3407
##     80       17.4073             nan     0.0100    0.2523
##    100       13.4865             nan     0.0100    0.1455
##    120       10.7289             nan     0.0100    0.1234
##    140        8.7456             nan     0.0100    0.0729
##    160        7.3458             nan     0.0100    0.0321
##    180        6.3556             nan     0.0100    0.0391
##    200        5.6343             nan     0.0100    0.0225
##    220        5.0878             nan     0.0100    0.0140
##    240        4.6723             nan     0.0100    0.0090
##    260        4.3701             nan     0.0100    0.0021
##    280        4.1307             nan     0.0100    0.0032
##    300        3.9457             nan     0.0100    0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.0601             nan     0.0100    0.9100
##      2       59.0133             nan     0.0100    1.1054
##      3       57.9875             nan     0.0100    0.8578
##      4       57.0170             nan     0.0100    0.9104
##      5       56.1023             nan     0.0100    0.9718
##      6       55.1425             nan     0.0100    0.7635
##      7       54.2256             nan     0.0100    1.0057
##      8       53.3202             nan     0.0100    0.9799
##      9       52.3989             nan     0.0100    0.8590
##     10       51.5373             nan     0.0100    0.7083
##     20       43.6384             nan     0.0100    0.7752
##     40       31.6797             nan     0.0100    0.4688
##     60       23.5365             nan     0.0100    0.3472
##     80       17.8371             nan     0.0100    0.2231
##    100       13.8891             nan     0.0100    0.1541
##    120       11.0696             nan     0.0100    0.1152
##    140        9.0807             nan     0.0100    0.0720
##    160        7.6880             nan     0.0100    0.0510
##    180        6.6640             nan     0.0100    0.0385
##    200        5.9594             nan     0.0100    0.0264
##    220        5.4381             nan     0.0100    0.0192
##    240        5.0597             nan     0.0100    0.0094
##    260        4.7629             nan     0.0100    0.0097
##    280        4.5301             nan     0.0100    0.0022
##    300        4.3435             nan     0.0100    0.0054
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.0686             nan     0.0500    3.9884
##      2       53.6808             nan     0.0500    3.2459
##      3       50.5747             nan     0.0500    3.0759
##      4       47.9277             nan     0.0500    2.7992
##      5       45.2158             nan     0.0500    2.5056
##      6       42.6245             nan     0.0500    2.3621
##      7       40.3680             nan     0.0500    2.0140
##      8       38.4157             nan     0.0500    1.9442
##      9       36.4579             nan     0.0500    1.6386
##     10       34.5504             nan     0.0500    1.8207
##     20       21.9010             nan     0.0500    0.6870
##     40       11.8403             nan     0.0500    0.2771
##     60        7.9130             nan     0.0500    0.1087
##     80        6.1085             nan     0.0500    0.0383
##    100        5.2288             nan     0.0500    0.0210
##    120        4.8862             nan     0.0500   -0.0014
##    140        4.6371             nan     0.0500   -0.0030
##    160        4.4916             nan     0.0500   -0.0114
##    180        4.4054             nan     0.0500   -0.0071
##    200        4.3184             nan     0.0500   -0.0503
##    220        4.2497             nan     0.0500   -0.0082
##    240        4.1732             nan     0.0500   -0.0034
##    260        4.1137             nan     0.0500   -0.0073
##    280        4.0693             nan     0.0500   -0.0171
##    300        4.0048             nan     0.0500   -0.0129
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.3032             nan     0.0500    3.4137
##      2       53.8451             nan     0.0500    3.3644
##      3       50.8419             nan     0.0500    3.0635
##      4       48.1059             nan     0.0500    2.8637
##      5       45.4951             nan     0.0500    2.6218
##      6       43.2009             nan     0.0500    2.2962
##      7       41.0027             nan     0.0500    2.1720
##      8       38.9278             nan     0.0500    2.3258
##      9       37.0991             nan     0.0500    1.1583
##     10       35.2996             nan     0.0500    1.7821
##     20       22.7003             nan     0.0500    0.7887
##     40       11.8425             nan     0.0500    0.2335
##     60        7.7871             nan     0.0500    0.0997
##     80        6.0970             nan     0.0500    0.0128
##    100        5.2797             nan     0.0500    0.0058
##    120        4.9291             nan     0.0500    0.0113
##    140        4.7586             nan     0.0500    0.0053
##    160        4.6136             nan     0.0500    0.0043
##    180        4.5174             nan     0.0500   -0.0162
##    200        4.4309             nan     0.0500   -0.0119
##    220        4.3630             nan     0.0500   -0.0095
##    240        4.3077             nan     0.0500   -0.0080
##    260        4.2591             nan     0.0500   -0.0239
##    280        4.2061             nan     0.0500   -0.0121
##    300        4.1674             nan     0.0500   -0.0157
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.0147             nan     0.0500    3.6156
##      2       53.6643             nan     0.0500    3.2812
##      3       50.5825             nan     0.0500    3.3877
##      4       47.7219             nan     0.0500    2.8477
##      5       44.9022             nan     0.0500    2.2341
##      6       42.7963             nan     0.0500    1.9367
##      7       40.2805             nan     0.0500    2.1741
##      8       38.2368             nan     0.0500    1.8505
##      9       36.2669             nan     0.0500    1.8008
##     10       34.6615             nan     0.0500    1.6843
##     20       22.1453             nan     0.0500    0.9306
##     40       11.9112             nan     0.0500    0.2382
##     60        8.0019             nan     0.0500    0.1019
##     80        6.2892             nan     0.0500    0.0461
##    100        5.4175             nan     0.0500    0.0038
##    120        5.0576             nan     0.0500   -0.0143
##    140        4.8511             nan     0.0500   -0.0234
##    160        4.7268             nan     0.0500   -0.0096
##    180        4.6364             nan     0.0500   -0.0134
##    200        4.5401             nan     0.0500   -0.0005
##    220        4.4543             nan     0.0500   -0.0058
##    240        4.3968             nan     0.0500   -0.0017
##    260        4.3379             nan     0.0500   -0.0115
##    280        4.2758             nan     0.0500   -0.0209
##    300        4.2173             nan     0.0500   -0.0118
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.9983             nan     0.0500    4.7744
##      2       51.3313             nan     0.0500    4.1629
##      3       47.2558             nan     0.0500    3.5812
##      4       43.5151             nan     0.0500    3.8992
##      5       40.3991             nan     0.0500    3.1273
##      6       37.2898             nan     0.0500    3.1562
##      7       34.5723             nan     0.0500    2.4273
##      8       32.0515             nan     0.0500    2.3127
##      9       29.7021             nan     0.0500    2.3771
##     10       27.6310             nan     0.0500    2.0703
##     20       14.6352             nan     0.0500    0.9613
##     40        6.4259             nan     0.0500    0.1341
##     60        4.3818             nan     0.0500    0.0446
##     80        3.7663             nan     0.0500   -0.0070
##    100        3.4074             nan     0.0500   -0.0090
##    120        3.1325             nan     0.0500   -0.0145
##    140        2.9468             nan     0.0500   -0.0280
##    160        2.7760             nan     0.0500   -0.0238
##    180        2.6049             nan     0.0500   -0.0169
##    200        2.4491             nan     0.0500   -0.0011
##    220        2.3390             nan     0.0500   -0.0172
##    240        2.2102             nan     0.0500   -0.0116
##    260        2.0966             nan     0.0500   -0.0148
##    280        2.0150             nan     0.0500   -0.0185
##    300        1.9287             nan     0.0500   -0.0253
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.1514             nan     0.0500    5.2119
##      2       51.9451             nan     0.0500    4.2393
##      3       47.9775             nan     0.0500    4.0969
##      4       44.1486             nan     0.0500    3.7577
##      5       40.9451             nan     0.0500    3.0020
##      6       37.9026             nan     0.0500    2.8061
##      7       35.1760             nan     0.0500    2.3815
##      8       32.5125             nan     0.0500    2.5488
##      9       30.0636             nan     0.0500    2.3018
##     10       27.9132             nan     0.0500    2.0051
##     20       14.8562             nan     0.0500    0.8340
##     40        6.5387             nan     0.0500    0.1066
##     60        4.5509             nan     0.0500    0.0100
##     80        3.9787             nan     0.0500   -0.0175
##    100        3.6682             nan     0.0500   -0.0467
##    120        3.4519             nan     0.0500   -0.0461
##    140        3.2717             nan     0.0500   -0.0283
##    160        3.0827             nan     0.0500   -0.0097
##    180        2.9268             nan     0.0500   -0.0071
##    200        2.8430             nan     0.0500   -0.0125
##    220        2.7379             nan     0.0500   -0.0200
##    240        2.6309             nan     0.0500   -0.0050
##    260        2.5530             nan     0.0500   -0.0132
##    280        2.4837             nan     0.0500   -0.0016
##    300        2.3895             nan     0.0500   -0.0092
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.1639             nan     0.0500    4.8764
##      2       51.9149             nan     0.0500    4.3815
##      3       47.8528             nan     0.0500    3.8368
##      4       43.9098             nan     0.0500    4.2040
##      5       40.3562             nan     0.0500    3.4795
##      6       37.2656             nan     0.0500    2.9575
##      7       34.6563             nan     0.0500    2.8532
##      8       32.1822             nan     0.0500    2.5280
##      9       29.8921             nan     0.0500    2.2462
##     10       27.9332             nan     0.0500    2.0290
##     20       14.9445             nan     0.0500    0.7714
##     40        6.9265             nan     0.0500    0.1457
##     60        4.9536             nan     0.0500    0.0053
##     80        4.3923             nan     0.0500   -0.0079
##    100        4.0726             nan     0.0500   -0.0061
##    120        3.8507             nan     0.0500   -0.0256
##    140        3.6559             nan     0.0500   -0.0085
##    160        3.5282             nan     0.0500   -0.0292
##    180        3.4002             nan     0.0500   -0.0157
##    200        3.2852             nan     0.0500   -0.0093
##    220        3.1708             nan     0.0500   -0.0330
##    240        3.0818             nan     0.0500   -0.0126
##    260        2.9886             nan     0.0500   -0.0144
##    280        2.8937             nan     0.0500   -0.0188
##    300        2.7978             nan     0.0500   -0.0347
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.8288             nan     0.0500    4.9513
##      2       50.9527             nan     0.0500    4.3735
##      3       46.5176             nan     0.0500    4.4041
##      4       42.7446             nan     0.0500    3.2939
##      5       39.3044             nan     0.0500    3.2678
##      6       36.3042             nan     0.0500    3.0930
##      7       33.4629             nan     0.0500    3.0656
##      8       31.0432             nan     0.0500    2.3087
##      9       28.7961             nan     0.0500    2.3715
##     10       26.8025             nan     0.0500    2.2347
##     20       13.1550             nan     0.0500    0.9706
##     40        5.3738             nan     0.0500    0.0882
##     60        3.7584             nan     0.0500    0.0220
##     80        3.0958             nan     0.0500   -0.0205
##    100        2.7100             nan     0.0500   -0.0301
##    120        2.4000             nan     0.0500   -0.0071
##    140        2.1467             nan     0.0500   -0.0151
##    160        1.9476             nan     0.0500   -0.0081
##    180        1.7633             nan     0.0500   -0.0275
##    200        1.6450             nan     0.0500   -0.0154
##    220        1.5266             nan     0.0500   -0.0122
##    240        1.4177             nan     0.0500   -0.0093
##    260        1.3207             nan     0.0500   -0.0064
##    280        1.2275             nan     0.0500   -0.0080
##    300        1.1415             nan     0.0500   -0.0056
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.1531             nan     0.0500    5.0049
##      2       51.4255             nan     0.0500    4.6784
##      3       47.0509             nan     0.0500    4.1250
##      4       43.2279             nan     0.0500    4.0666
##      5       39.8600             nan     0.0500    3.4924
##      6       36.4985             nan     0.0500    3.2027
##      7       33.6636             nan     0.0500    3.1167
##      8       30.9767             nan     0.0500    2.3953
##      9       28.6653             nan     0.0500    2.0766
##     10       26.4360             nan     0.0500    2.1462
##     20       13.2797             nan     0.0500    0.7163
##     40        5.5330             nan     0.0500    0.0784
##     60        3.9457             nan     0.0500   -0.0058
##     80        3.3950             nan     0.0500    0.0058
##    100        3.0695             nan     0.0500   -0.0232
##    120        2.8488             nan     0.0500   -0.0168
##    140        2.6675             nan     0.0500   -0.0203
##    160        2.4962             nan     0.0500   -0.0142
##    180        2.3408             nan     0.0500   -0.0338
##    200        2.2129             nan     0.0500   -0.0150
##    220        2.0818             nan     0.0500   -0.0183
##    240        1.9452             nan     0.0500   -0.0162
##    260        1.8487             nan     0.0500   -0.0238
##    280        1.7450             nan     0.0500   -0.0091
##    300        1.6702             nan     0.0500   -0.0106
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.7093             nan     0.0500    4.3436
##      2       51.0978             nan     0.0500    4.5788
##      3       46.9783             nan     0.0500    3.5963
##      4       43.1662             nan     0.0500    3.5175
##      5       39.5754             nan     0.0500    3.7428
##      6       36.6279             nan     0.0500    3.1279
##      7       33.8223             nan     0.0500    2.7487
##      8       31.1996             nan     0.0500    2.7334
##      9       28.6260             nan     0.0500    2.3199
##     10       26.5668             nan     0.0500    1.9730
##     20       13.3661             nan     0.0500    0.8031
##     40        5.8876             nan     0.0500    0.1000
##     60        4.3764             nan     0.0500    0.0065
##     80        3.8676             nan     0.0500   -0.0103
##    100        3.6180             nan     0.0500   -0.0241
##    120        3.3918             nan     0.0500   -0.0208
##    140        3.1635             nan     0.0500   -0.0167
##    160        2.9961             nan     0.0500   -0.0108
##    180        2.8291             nan     0.0500   -0.0169
##    200        2.6893             nan     0.0500   -0.0197
##    220        2.5717             nan     0.0500   -0.0157
##    240        2.4800             nan     0.0500   -0.0203
##    260        2.3711             nan     0.0500   -0.0177
##    280        2.2768             nan     0.0500   -0.0084
##    300        2.1801             nan     0.0500   -0.0170
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.8677             nan     0.1000    7.6444
##      2       47.8801             nan     0.1000    5.9888
##      3       42.7878             nan     0.1000    5.1558
##      4       38.5760             nan     0.1000    4.3065
##      5       34.6497             nan     0.1000    3.5810
##      6       31.1730             nan     0.1000    3.1862
##      7       28.3652             nan     0.1000    2.7487
##      8       25.9222             nan     0.1000    2.6672
##      9       24.0243             nan     0.1000    1.7219
##     10       22.1442             nan     0.1000    1.9251
##     20       11.8167             nan     0.1000    0.4500
##     40        6.0379             nan     0.1000    0.0177
##     60        4.8325             nan     0.1000   -0.0742
##     80        4.4516             nan     0.1000   -0.0102
##    100        4.2707             nan     0.1000   -0.0181
##    120        4.1316             nan     0.1000   -0.0186
##    140        4.0287             nan     0.1000   -0.0507
##    160        3.9035             nan     0.1000   -0.0315
##    180        3.7980             nan     0.1000   -0.0296
##    200        3.7133             nan     0.1000   -0.0530
##    220        3.6525             nan     0.1000   -0.0258
##    240        3.5851             nan     0.1000   -0.0149
##    260        3.5221             nan     0.1000   -0.0199
##    280        3.4688             nan     0.1000   -0.0119
##    300        3.3949             nan     0.1000   -0.0398
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.5084             nan     0.1000    7.1754
##      2       47.2144             nan     0.1000    6.3245
##      3       43.0341             nan     0.1000    3.7974
##      4       38.7386             nan     0.1000    4.5051
##      5       34.9836             nan     0.1000    3.8099
##      6       31.5748             nan     0.1000    3.1828
##      7       29.0431             nan     0.1000    2.8182
##      8       26.6336             nan     0.1000    2.0474
##      9       24.3920             nan     0.1000    1.8482
##     10       22.3207             nan     0.1000    1.8764
##     20       11.7955             nan     0.1000    0.4241
##     40        6.0267             nan     0.1000    0.0558
##     60        4.9952             nan     0.1000    0.0030
##     80        4.7756             nan     0.1000   -0.0193
##    100        4.5658             nan     0.1000   -0.0239
##    120        4.4196             nan     0.1000   -0.0105
##    140        4.2895             nan     0.1000   -0.0256
##    160        4.2042             nan     0.1000   -0.0218
##    180        4.1279             nan     0.1000   -0.0395
##    200        4.0555             nan     0.1000   -0.0101
##    220        3.9550             nan     0.1000   -0.0309
##    240        3.9001             nan     0.1000   -0.0213
##    260        3.8208             nan     0.1000   -0.0157
##    280        3.7665             nan     0.1000   -0.0222
##    300        3.7020             nan     0.1000   -0.0197
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.0329             nan     0.1000    6.6900
##      2       47.0447             nan     0.1000    5.9615
##      3       41.9518             nan     0.1000    4.8319
##      4       37.8651             nan     0.1000    3.5815
##      5       33.9618             nan     0.1000    3.5096
##      6       31.0610             nan     0.1000    2.9142
##      7       28.3769             nan     0.1000    2.2712
##      8       26.1858             nan     0.1000    1.7849
##      9       23.8608             nan     0.1000    1.9066
##     10       21.9904             nan     0.1000    1.6938
##     20       12.0102             nan     0.1000    0.4134
##     40        6.4074             nan     0.1000    0.0925
##     60        5.3255             nan     0.1000    0.0227
##     80        4.9871             nan     0.1000   -0.0068
##    100        4.7646             nan     0.1000   -0.0191
##    120        4.6076             nan     0.1000   -0.0103
##    140        4.4954             nan     0.1000   -0.0474
##    160        4.4027             nan     0.1000   -0.0107
##    180        4.3389             nan     0.1000   -0.0316
##    200        4.2413             nan     0.1000   -0.0377
##    220        4.1636             nan     0.1000   -0.0502
##    240        4.0818             nan     0.1000   -0.0055
##    260        4.0279             nan     0.1000   -0.0180
##    280        3.9776             nan     0.1000   -0.0232
##    300        3.9315             nan     0.1000   -0.0179
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.6074             nan     0.1000    9.4813
##      2       43.7770             nan     0.1000    7.1965
##      3       37.2460             nan     0.1000    6.6488
##      4       32.2083             nan     0.1000    4.7082
##      5       27.8199             nan     0.1000    3.7118
##      6       24.2310             nan     0.1000    3.6556
##      7       21.0037             nan     0.1000    2.6350
##      8       18.3308             nan     0.1000    2.4150
##      9       16.3724             nan     0.1000    1.6787
##     10       14.4304             nan     0.1000    1.7090
##     20        6.3774             nan     0.1000    0.3449
##     40        3.7627             nan     0.1000   -0.0162
##     60        3.1631             nan     0.1000   -0.0807
##     80        2.7504             nan     0.1000   -0.0546
##    100        2.4414             nan     0.1000   -0.0236
##    120        2.2242             nan     0.1000   -0.0403
##    140        2.0076             nan     0.1000   -0.0287
##    160        1.8313             nan     0.1000   -0.0143
##    180        1.6903             nan     0.1000   -0.0091
##    200        1.5727             nan     0.1000   -0.0307
##    220        1.4740             nan     0.1000   -0.0273
##    240        1.3888             nan     0.1000   -0.0209
##    260        1.3076             nan     0.1000   -0.0210
##    280        1.2323             nan     0.1000   -0.0126
##    300        1.1621             nan     0.1000   -0.0230
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.5395             nan     0.1000    8.8021
##      2       43.8727             nan     0.1000    7.0956
##      3       37.4693             nan     0.1000    6.4202
##      4       32.2181             nan     0.1000    3.9319
##      5       27.8648             nan     0.1000    4.1166
##      6       23.8674             nan     0.1000    3.5381
##      7       20.7736             nan     0.1000    2.7611
##      8       18.0459             nan     0.1000    2.5673
##      9       15.9883             nan     0.1000    2.1072
##     10       14.3939             nan     0.1000    1.4549
##     20        6.4929             nan     0.1000    0.2404
##     40        4.0851             nan     0.1000   -0.0125
##     60        3.5865             nan     0.1000   -0.0031
##     80        3.2412             nan     0.1000   -0.0388
##    100        2.9754             nan     0.1000   -0.0212
##    120        2.7093             nan     0.1000   -0.0204
##    140        2.5365             nan     0.1000   -0.0146
##    160        2.3725             nan     0.1000   -0.0258
##    180        2.2235             nan     0.1000   -0.0434
##    200        2.1015             nan     0.1000   -0.0280
##    220        1.9917             nan     0.1000   -0.0262
##    240        1.8868             nan     0.1000   -0.0275
##    260        1.7774             nan     0.1000   -0.0284
##    280        1.6900             nan     0.1000   -0.0134
##    300        1.6035             nan     0.1000   -0.0230
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.0311             nan     0.1000    9.5023
##      2       44.4135             nan     0.1000    7.6719
##      3       38.3621             nan     0.1000    6.1439
##      4       33.0221             nan     0.1000    5.6061
##      5       28.9102             nan     0.1000    4.2926
##      6       24.8364             nan     0.1000    4.2160
##      7       21.6394             nan     0.1000    3.0735
##      8       19.2988             nan     0.1000    2.5517
##      9       17.1599             nan     0.1000    2.1506
##     10       15.3016             nan     0.1000    1.5437
##     20        6.8800             nan     0.1000    0.2779
##     40        4.3824             nan     0.1000    0.0188
##     60        3.7977             nan     0.1000   -0.0475
##     80        3.4524             nan     0.1000   -0.0375
##    100        3.2551             nan     0.1000   -0.0109
##    120        3.0557             nan     0.1000   -0.0303
##    140        2.8644             nan     0.1000   -0.0209
##    160        2.6966             nan     0.1000   -0.0299
##    180        2.5677             nan     0.1000   -0.0218
##    200        2.4332             nan     0.1000   -0.0197
##    220        2.3221             nan     0.1000   -0.0402
##    240        2.2108             nan     0.1000   -0.0508
##    260        2.1140             nan     0.1000   -0.0159
##    280        2.0249             nan     0.1000   -0.0160
##    300        1.9395             nan     0.1000   -0.0182
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.9306             nan     0.1000   10.1220
##      2       42.4697             nan     0.1000    7.8818
##      3       35.5855             nan     0.1000    6.9267
##      4       30.2626             nan     0.1000    4.8325
##      5       25.4988             nan     0.1000    3.9239
##      6       21.7846             nan     0.1000    3.7323
##      7       19.1754             nan     0.1000    2.8123
##      8       16.5570             nan     0.1000    2.3310
##      9       14.4382             nan     0.1000    2.0743
##     10       12.6717             nan     0.1000    1.6600
##     20        5.2304             nan     0.1000    0.2012
##     40        3.1735             nan     0.1000    0.0042
##     60        2.4921             nan     0.1000   -0.0195
##     80        2.0801             nan     0.1000   -0.0476
##    100        1.7569             nan     0.1000   -0.0170
##    120        1.5026             nan     0.1000   -0.0407
##    140        1.3099             nan     0.1000   -0.0236
##    160        1.1569             nan     0.1000   -0.0164
##    180        1.0250             nan     0.1000   -0.0138
##    200        0.9268             nan     0.1000   -0.0218
##    220        0.8406             nan     0.1000   -0.0189
##    240        0.7582             nan     0.1000   -0.0075
##    260        0.6847             nan     0.1000   -0.0115
##    280        0.6190             nan     0.1000   -0.0160
##    300        0.5659             nan     0.1000   -0.0145
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.7677             nan     0.1000   10.3466
##      2       42.4192             nan     0.1000    7.7894
##      3       35.4452             nan     0.1000    6.0542
##      4       30.0178             nan     0.1000    4.9837
##      5       25.8880             nan     0.1000    3.6263
##      6       22.3799             nan     0.1000    3.0674
##      7       19.0905             nan     0.1000    3.0272
##      8       16.4027             nan     0.1000    2.4685
##      9       14.3910             nan     0.1000    2.2374
##     10       12.7889             nan     0.1000    1.6632
##     20        5.5572             nan     0.1000    0.0503
##     40        3.5114             nan     0.1000   -0.0139
##     60        2.9355             nan     0.1000   -0.0290
##     80        2.5536             nan     0.1000   -0.0541
##    100        2.3081             nan     0.1000   -0.0475
##    120        2.0764             nan     0.1000   -0.0234
##    140        1.9032             nan     0.1000   -0.0274
##    160        1.7320             nan     0.1000   -0.0303
##    180        1.5830             nan     0.1000   -0.0210
##    200        1.4703             nan     0.1000   -0.0157
##    220        1.3467             nan     0.1000   -0.0215
##    240        1.2332             nan     0.1000   -0.0204
##    260        1.1301             nan     0.1000   -0.0344
##    280        1.0400             nan     0.1000   -0.0146
##    300        0.9682             nan     0.1000   -0.0131
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.1003             nan     0.1000    9.3546
##      2       42.9178             nan     0.1000    7.4694
##      3       36.3539             nan     0.1000    6.5993
##      4       31.0834             nan     0.1000    5.2886
##      5       26.5855             nan     0.1000    4.1206
##      6       22.5874             nan     0.1000    3.6964
##      7       19.4913             nan     0.1000    3.1826
##      8       16.9955             nan     0.1000    2.6055
##      9       14.9757             nan     0.1000    1.9573
##     10       13.3151             nan     0.1000    1.5012
##     20        5.9095             nan     0.1000    0.2224
##     40        3.9130             nan     0.1000   -0.0003
##     60        3.3758             nan     0.1000   -0.0229
##     80        3.0844             nan     0.1000   -0.0496
##    100        2.8037             nan     0.1000   -0.0346
##    120        2.5693             nan     0.1000   -0.0050
##    140        2.3889             nan     0.1000   -0.0174
##    160        2.2223             nan     0.1000   -0.0345
##    180        2.0664             nan     0.1000   -0.0389
##    200        1.9129             nan     0.1000   -0.0247
##    220        1.7844             nan     0.1000   -0.0349
##    240        1.6862             nan     0.1000   -0.0264
##    260        1.5918             nan     0.1000   -0.0147
##    280        1.4980             nan     0.1000   -0.0341
##    300        1.3987             nan     0.1000   -0.0158
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.3660             nan     0.0500    4.8121
##      2       51.4808             nan     0.0500    4.4977
##      3       47.0687             nan     0.0500    4.8036
##      4       43.1309             nan     0.0500    3.9498
##      5       39.6542             nan     0.0500    3.3338
##      6       36.6243             nan     0.0500    2.9456
##      7       33.6848             nan     0.0500    2.8345
##      8       31.1140             nan     0.0500    2.5885
##      9       28.6350             nan     0.0500    2.2721
##     10       26.4003             nan     0.0500    1.9125
##     20       12.8657             nan     0.0500    0.7933
##     40        5.2493             nan     0.0500    0.1350
##     60        3.5775             nan     0.0500   -0.0106
##     80        2.9925             nan     0.0500   -0.0117
##    100        2.6640             nan     0.0500   -0.0236
##    120        2.4214             nan     0.0500   -0.0268
##    140        2.1867             nan     0.0500   -0.0006
##    160        1.9970             nan     0.0500   -0.0212
##    180        1.8204             nan     0.0500   -0.0191
##    200        1.6800             nan     0.0500   -0.0040
##    220        1.5605             nan     0.0500   -0.0062
##    240        1.4368             nan     0.0500   -0.0133
##    260        1.3442             nan     0.0500   -0.0080
##    280        1.2618             nan     0.0500   -0.0061
##    300        1.1867             nan     0.0500   -0.0132
##################################
# Reporting the cross-validation results
# for the GBM model
##################################
GBM_Tune
## Stochastic Gradient Boosting 
## 
## 294 samples
##   5 predictor
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 266, 264, 265, 264, 265, 266, ... 
## Resampling results across tuning parameters:
## 
##   shrinkage  interaction.depth  n.minobsinnode  n.trees  RMSE      Rsquared 
##   0.01       1                   5              100      4.810935  0.8220024
##   0.01       1                   5              200      3.591342  0.8649364
##   0.01       1                   5              300      2.996879  0.8894673
##   0.01       1                  10              100      4.804888  0.8245577
##   0.01       1                  10              200      3.594237  0.8642448
##   0.01       1                  10              300      2.996239  0.8897373
##   0.01       1                  15              100      4.801958  0.8224158
##   0.01       1                  15              200      3.580953  0.8641242
##   0.01       1                  15              300      2.999924  0.8879375
##   0.01       3                   5              100      3.973208  0.8904896
##   0.01       3                   5              200      2.764060  0.9112249
##   0.01       3                   5              300      2.425116  0.9185948
##   0.01       3                  10              100      3.970773  0.8893448
##   0.01       3                  10              200      2.771515  0.9103313
##   0.01       3                  10              300      2.441187  0.9176932
##   0.01       3                  15              100      3.990870  0.8850612
##   0.01       3                  15              200      2.824472  0.9047488
##   0.01       3                  15              300      2.489460  0.9135700
##   0.01       5                   5              100      3.765540  0.9072375
##   0.01       5                   5              200      2.618322  0.9166507
##   0.01       5                   5              300      2.380487  0.9195383
##   0.01       5                  10              100      3.761667  0.9075920
##   0.01       5                  10              200      2.610134  0.9173829
##   0.01       5                  10              300      2.374509  0.9205702
##   0.01       5                  15              100      3.788274  0.9029016
##   0.01       5                  15              200      2.658002  0.9129307
##   0.01       5                  15              300      2.418481  0.9169741
##   0.05       1                   5              100      2.532506  0.9087920
##   0.05       1                   5              200      2.439447  0.9148728
##   0.05       1                   5              300      2.437366  0.9142320
##   0.05       1                  10              100      2.503174  0.9128347
##   0.05       1                  10              200      2.378897  0.9209685
##   0.05       1                  10              300      2.370010  0.9210478
##   0.05       1                  15              100      2.563312  0.9068743
##   0.05       1                  15              200      2.434734  0.9152549
##   0.05       1                  15              300      2.438963  0.9157699
##   0.05       3                   5              100      2.346351  0.9206420
##   0.05       3                   5              200      2.345393  0.9199519
##   0.05       3                   5              300      2.327349  0.9207466
##   0.05       3                  10              100      2.360410  0.9194875
##   0.05       3                  10              200      2.372344  0.9180728
##   0.05       3                  10              300      2.358254  0.9191602
##   0.05       3                  15              100      2.358306  0.9181427
##   0.05       3                  15              200      2.357688  0.9187985
##   0.05       3                  15              300      2.366932  0.9176364
##   0.05       5                   5              100      2.344008  0.9188591
##   0.05       5                   5              200      2.333080  0.9188954
##   0.05       5                   5              300      2.319410  0.9198973
##   0.05       5                  10              100      2.386873  0.9173964
##   0.05       5                  10              200      2.385332  0.9171571
##   0.05       5                  10              300      2.376674  0.9172431
##   0.05       5                  15              100      2.374062  0.9175198
##   0.05       5                  15              200      2.358935  0.9191052
##   0.05       5                  15              300      2.360735  0.9193228
##   0.10       1                   5              100      2.449071  0.9157931
##   0.10       1                   5              200      2.452391  0.9146947
##   0.10       1                   5              300      2.470079  0.9134424
##   0.10       1                  10              100      2.465371  0.9133767
##   0.10       1                  10              200      2.463359  0.9139698
##   0.10       1                  10              300      2.445996  0.9142583
##   0.10       1                  15              100      2.432597  0.9151404
##   0.10       1                  15              200      2.455241  0.9150903
##   0.10       1                  15              300      2.434320  0.9161904
##   0.10       3                   5              100      2.352778  0.9177282
##   0.10       3                   5              200      2.335391  0.9186142
##   0.10       3                   5              300      2.335459  0.9178562
##   0.10       3                  10              100      2.386385  0.9169080
##   0.10       3                  10              200      2.348308  0.9189082
##   0.10       3                  10              300      2.339177  0.9188534
##   0.10       3                  15              100      2.363800  0.9195673
##   0.10       3                  15              200      2.380596  0.9181203
##   0.10       3                  15              300      2.362293  0.9199136
##   0.10       5                   5              100      2.393913  0.9154127
##   0.10       5                   5              200      2.391989  0.9153164
##   0.10       5                   5              300      2.398330  0.9139535
##   0.10       5                  10              100      2.376873  0.9169394
##   0.10       5                  10              200      2.361392  0.9175143
##   0.10       5                  10              300      2.392849  0.9160077
##   0.10       5                  15              100      2.429391  0.9143777
##   0.10       5                  15              200      2.434540  0.9141983
##   0.10       5                  15              300      2.452960  0.9136757
##   MAE     
##   3.877301
##   2.888462
##   2.363814
##   3.869919
##   2.880853
##   2.347499
##   3.863080
##   2.868756
##   2.344566
##   3.273956
##   2.172907
##   1.849299
##   3.274361
##   2.175127
##   1.838932
##   3.278500
##   2.205304
##   1.878976
##   3.103002
##   2.055751
##   1.801944
##   3.090197
##   2.035921
##   1.768429
##   3.111034
##   2.050567
##   1.799572
##   1.905685
##   1.804011
##   1.782111
##   1.887731
##   1.766298
##   1.747344
##   1.960129
##   1.813449
##   1.805008
##   1.755183
##   1.746585
##   1.732468
##   1.738475
##   1.730635
##   1.716780
##   1.749207
##   1.731617
##   1.730280
##   1.725602
##   1.714908
##   1.716469
##   1.773769
##   1.756986
##   1.742463
##   1.768412
##   1.744998
##   1.746614
##   1.821177
##   1.802574
##   1.807528
##   1.842272
##   1.826997
##   1.800826
##   1.783575
##   1.795195
##   1.779606
##   1.738886
##   1.740522
##   1.747177
##   1.739180
##   1.719281
##   1.716109
##   1.760685
##   1.758236
##   1.747422
##   1.749146
##   1.762114
##   1.763512
##   1.746935
##   1.736254
##   1.767303
##   1.792245
##   1.787265
##   1.807362
## 
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were n.trees = 300, interaction.depth =
##  5, shrinkage = 0.05 and n.minobsinnode = 5.
GBM_Tune$finalModel
## A gradient boosted model with gaussian loss function.
## 300 iterations were performed.
## There were 5 predictors of which 5 had non-zero influence.
(GBM_Tune_RMSE <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
                              GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
                              GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
                              GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
                 c("RMSE")])
## [1] 2.31941
(GBM_Tune_Rsquared <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
                              GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
                              GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
                              GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
                 c("Rsquared")])
## [1] 0.9198973
(GBM_Tune_MAE <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
                              GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
                              GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
                              GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
                 c("MAE")])
## [1] 1.716469
##################################
# Identifying and plotting the
# best model predictors
# for the GBM model
##################################
GBM_VarImp <- varImp(GBM_Tune, scale = TRUE)
plot(GBM_VarImp, 
     scales=list(y=list(cex = .95)),
     main="Ranked Variable Importance : GBM",
     xlab="Scaled Variable Importance Metrics",
     ylab="Predictors",
     cex=2,
     origin=0,
     alpha=0.45)


1.3.6.2 Random Forest (RF)


Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the RF model
##################################
RF_Grid = data.frame(mtry = c(100, 200, 300, 400, 500, 
                              600, 700, 800, 900, 1000))

##################################
# Running the RF model
# by setting the caret method to 'RF'
##################################
set.seed(12345678)
RF_Tune <- train(x = MD.Model.Predictors, 
                 y = MD$LIFEXP,
                 method = "rf",
                 tuneGrid = RF_Grid,
                 trControl = KFold_Control)

##################################
# Reporting the cross-validation results
# for the RF model
##################################
RF_Tune
## Random Forest 
## 
## 294 samples
##   5 predictor
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 266, 264, 265, 264, 265, 266, ... 
## Resampling results across tuning parameters:
## 
##   mtry  RMSE      Rsquared   MAE     
##    100  2.469134  0.9069495  1.804822
##    200  2.468106  0.9065615  1.798157
##    300  2.467568  0.9067371  1.800105
##    400  2.454183  0.9077840  1.785219
##    500  2.485343  0.9058410  1.814855
##    600  2.489623  0.9053762  1.817485
##    700  2.480923  0.9058562  1.809700
##    800  2.476728  0.9064718  1.806681
##    900  2.476405  0.9065263  1.801839
##   1000  2.492884  0.9053344  1.814941
## 
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 400.
RF_Tune$finalModel
## 
## Call:
##  randomForest(x = x, y = y, mtry = param$mtry) 
##                Type of random forest: regression
##                      Number of trees: 500
## No. of variables tried at each split: 5
## 
##           Mean of squared residuals: 6.28444
##                     % Var explained: 89.77
(RF_Tune_RMSE <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
                 c("RMSE")])
## [1] 2.454183
(RF_Tune_Rsquared <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
                 c("Rsquared")])
## [1] 0.907784
(RF_Tune_MAE <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
                 c("MAE")])
## [1] 1.785219
##################################
# Identifying and plotting the
# best model predictors
# for the RF model
##################################
RF_VarImp <- varImp(RF_Tune, scale = TRUE)
plot(RF_VarImp, 
     scales=list(y=list(cex = .95)),
     main="Ranked Variable Importance : RF",
     xlab="Scaled Variable Importance Metrics",
     ylab="Predictors",
     cex=2,
     origin=0,
     alpha=0.45)


1.3.6.3 Neural Network (NN)


Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the NN model
##################################
NN_Grid = expand.grid(size = c(2, 5, 10, 15, 20), 
                      decay = c(0, 0.1, 0.001, 0.0001, 0.00001))

##################################
# Running the NN model
# by setting the caret method to 'NN'
##################################
set.seed(12345678)
NN_Tune <- train(x = MD.Model.Predictors,
                 y = MD$LIFEXP,
                 method = "nnet",
                 linout = TRUE,
                 preProcess = c('center', 'scale'),
                 maxit = 500,
                 tuneGrid = NN_Grid,
                 trControl = KFold_Control)
## # weights:  15
## initial  value 1406972.747928 
## iter  10 value 17306.731946
## final  value 16734.547507 
## converged
## # weights:  36
## initial  value 1386115.389324 
## iter  10 value 3747.659411
## iter  20 value 1974.438861
## iter  30 value 1480.043876
## iter  40 value 1203.077370
## iter  50 value 1099.257974
## iter  60 value 1060.936116
## iter  70 value 1040.946828
## iter  80 value 1034.781443
## iter  90 value 1026.625616
## iter 100 value 1006.773023
## iter 110 value 986.220318
## iter 120 value 957.503659
## iter 130 value 945.283708
## iter 140 value 934.378541
## iter 150 value 926.520491
## iter 160 value 924.816890
## iter 170 value 917.440515
## iter 180 value 914.467570
## iter 190 value 911.122985
## iter 200 value 909.759038
## iter 210 value 909.663170
## iter 220 value 909.372418
## iter 230 value 909.342318
## iter 240 value 908.941445
## iter 250 value 907.867688
## iter 260 value 907.334520
## iter 270 value 907.266447
## iter 280 value 907.253068
## iter 290 value 907.074096
## iter 300 value 906.765869
## final  value 906.754491 
## converged
## # weights:  71
## initial  value 1353444.522438 
## iter  10 value 1648.263520
## iter  20 value 1204.419231
## iter  30 value 1072.404973
## iter  40 value 996.768999
## iter  50 value 955.077615
## iter  60 value 926.091176
## iter  70 value 889.713680
## iter  80 value 835.505714
## iter  90 value 800.676805
## iter 100 value 777.747877
## iter 110 value 743.926912
## iter 120 value 705.465974
## iter 130 value 659.865568
## iter 140 value 625.813797
## iter 150 value 619.258581
## iter 160 value 616.155254
## iter 170 value 606.049414
## iter 180 value 595.699481
## iter 190 value 588.072845
## iter 200 value 580.056197
## iter 210 value 567.804995
## iter 220 value 562.505124
## iter 230 value 558.441302
## iter 240 value 553.771010
## iter 250 value 547.974068
## iter 260 value 542.919518
## iter 270 value 539.736711
## iter 280 value 535.595847
## iter 290 value 533.118204
## iter 300 value 532.333468
## iter 310 value 531.018371
## iter 320 value 529.518356
## iter 330 value 528.566091
## iter 340 value 527.798319
## iter 350 value 526.952978
## iter 360 value 525.626017
## iter 370 value 524.360393
## iter 380 value 521.255604
## iter 390 value 519.689711
## iter 400 value 518.712474
## iter 410 value 518.294930
## iter 420 value 518.007947
## iter 430 value 517.981217
## iter 440 value 517.980722
## iter 450 value 517.980321
## iter 460 value 517.979203
## iter 470 value 517.977310
## iter 480 value 517.968718
## iter 490 value 517.837477
## iter 500 value 517.662914
## final  value 517.662914 
## stopped after 500 iterations
## # weights:  106
## initial  value 1409418.840789 
## iter  10 value 1325.784047
## iter  20 value 1088.694235
## iter  30 value 962.449694
## iter  40 value 847.693836
## iter  50 value 782.145588
## iter  60 value 695.632632
## iter  70 value 627.861612
## iter  80 value 594.493835
## iter  90 value 575.059432
## iter 100 value 561.086218
## iter 110 value 534.329803
## iter 120 value 490.066511
## iter 130 value 465.480794
## iter 140 value 442.739698
## iter 150 value 431.119941
## iter 160 value 423.454706
## iter 170 value 415.513606
## iter 180 value 409.290801
## iter 190 value 404.673111
## iter 200 value 399.794798
## iter 210 value 396.095946
## iter 220 value 394.305084
## iter 230 value 393.367340
## iter 240 value 391.575001
## iter 250 value 388.956156
## iter 260 value 383.932899
## iter 270 value 372.170220
## iter 280 value 364.740221
## iter 290 value 359.509029
## iter 300 value 351.256116
## iter 310 value 341.675288
## iter 320 value 338.290422
## iter 330 value 336.223563
## iter 340 value 334.964544
## iter 350 value 334.068692
## iter 360 value 333.505675
## iter 370 value 332.808529
## iter 380 value 331.425240
## iter 390 value 329.613177
## iter 400 value 328.332686
## iter 410 value 327.775660
## iter 420 value 327.594785
## iter 430 value 327.531307
## iter 440 value 327.529235
## iter 450 value 327.522892
## iter 460 value 327.514081
## iter 470 value 327.500114
## iter 480 value 327.461898
## iter 490 value 327.224713
## iter 500 value 326.843868
## final  value 326.843868 
## stopped after 500 iterations
## # weights:  141
## initial  value 1398973.066738 
## iter  10 value 1686.016326
## iter  20 value 1127.060603
## iter  30 value 944.645340
## iter  40 value 822.849266
## iter  50 value 737.427430
## iter  60 value 671.350469
## iter  70 value 629.045609
## iter  80 value 594.349911
## iter  90 value 562.372525
## iter 100 value 515.929303
## iter 110 value 481.253627
## iter 120 value 442.570930
## iter 130 value 413.209126
## iter 140 value 389.418247
## iter 150 value 367.598205
## iter 160 value 352.650564
## iter 170 value 336.207834
## iter 180 value 323.775668
## iter 190 value 316.764159
## iter 200 value 310.562379
## iter 210 value 301.334935
## iter 220 value 294.104293
## iter 230 value 283.069245
## iter 240 value 273.404821
## iter 250 value 267.714611
## iter 260 value 262.482541
## iter 270 value 253.395413
## iter 280 value 247.847948
## iter 290 value 245.496879
## iter 300 value 244.307030
## iter 310 value 242.492200
## iter 320 value 240.667713
## iter 330 value 239.380813
## iter 340 value 237.968113
## iter 350 value 235.637956
## iter 360 value 229.843322
## iter 370 value 226.485616
## iter 380 value 222.461939
## iter 390 value 215.550406
## iter 400 value 211.480761
## iter 410 value 206.621390
## iter 420 value 201.630312
## iter 430 value 198.889249
## iter 440 value 195.740854
## iter 450 value 193.957590
## iter 460 value 192.173457
## iter 470 value 190.926555
## iter 480 value 189.716988
## iter 490 value 187.874512
## iter 500 value 186.725424
## final  value 186.725424 
## stopped after 500 iterations
## # weights:  15
## initial  value 1400381.846014 
## iter  10 value 7067.881683
## iter  20 value 6206.286589
## iter  30 value 3467.746758
## iter  40 value 2324.081266
## iter  50 value 2069.308343
## iter  60 value 1971.540882
## iter  70 value 1733.850963
## iter  80 value 1674.525653
## iter  90 value 1668.600766
## iter 100 value 1662.156476
## final  value 1662.044228 
## converged
## # weights:  36
## initial  value 1425263.534021 
## iter  10 value 19451.969836
## iter  20 value 9160.229645
## iter  30 value 7600.056376
## iter  40 value 6700.328755
## iter  50 value 4604.195084
## iter  60 value 3041.680951
## iter  70 value 2514.659972
## iter  80 value 2027.325192
## iter  90 value 1727.217162
## iter 100 value 1519.706835
## iter 110 value 1456.669525
## iter 120 value 1425.909883
## iter 130 value 1411.163155
## iter 140 value 1383.288110
## iter 150 value 1332.521492
## iter 160 value 1268.309496
## iter 170 value 1222.208689
## iter 180 value 1210.416153
## iter 190 value 1202.110126
## iter 200 value 1191.508554
## iter 210 value 1188.634218
## iter 220 value 1181.793186
## iter 230 value 1175.078267
## iter 240 value 1172.038537
## iter 250 value 1171.081216
## final  value 1171.037905 
## converged
## # weights:  71
## initial  value 1384131.732456 
## iter  10 value 1976.543920
## iter  20 value 1363.628476
## iter  30 value 1218.826059
## iter  40 value 1134.281800
## iter  50 value 1054.321612
## iter  60 value 1005.843954
## iter  70 value 969.627519
## iter  80 value 938.650414
## iter  90 value 921.522617
## iter 100 value 904.542103
## iter 110 value 894.117665
## iter 120 value 884.474109
## iter 130 value 878.770902
## iter 140 value 876.683479
## iter 150 value 872.973336
## iter 160 value 867.828572
## iter 170 value 863.333203
## iter 180 value 861.022376
## iter 190 value 859.544386
## iter 200 value 853.591475
## iter 210 value 850.086686
## iter 220 value 848.678059
## iter 230 value 846.452737
## iter 240 value 845.705613
## iter 250 value 845.518519
## iter 260 value 845.507494
## final  value 845.507466 
## converged
## # weights:  106
## initial  value 1414503.210071 
## iter  10 value 1343.433832
## iter  20 value 1146.624886
## iter  30 value 1023.869360
## iter  40 value 953.781776
## iter  50 value 886.929725
## iter  60 value 855.866975
## iter  70 value 828.138589
## iter  80 value 805.855024
## iter  90 value 791.941820
## iter 100 value 777.446265
## iter 110 value 764.281980
## iter 120 value 749.008650
## iter 130 value 736.414466
## iter 140 value 730.142448
## iter 150 value 723.423440
## iter 160 value 716.986154
## iter 170 value 711.967611
## iter 180 value 708.721393
## iter 190 value 701.550418
## iter 200 value 693.816749
## iter 210 value 688.987131
## iter 220 value 686.398192
## iter 230 value 684.483765
## iter 240 value 681.942192
## iter 250 value 680.032327
## iter 260 value 679.198732
## iter 270 value 678.903392
## iter 280 value 678.789425
## iter 290 value 678.684709
## iter 300 value 678.633211
## iter 310 value 678.624871
## iter 320 value 678.624009
## final  value 678.623923 
## converged
## # weights:  141
## initial  value 1366761.165719 
## iter  10 value 1673.371539
## iter  20 value 1286.191377
## iter  30 value 1155.802442
## iter  40 value 1068.274149
## iter  50 value 979.125330
## iter  60 value 923.191796
## iter  70 value 877.834055
## iter  80 value 825.655591
## iter  90 value 790.561121
## iter 100 value 763.789031
## iter 110 value 750.172786
## iter 120 value 735.780027
## iter 130 value 721.767504
## iter 140 value 709.501881
## iter 150 value 696.912583
## iter 160 value 681.545364
## iter 170 value 674.312137
## iter 180 value 670.400298
## iter 190 value 666.222116
## iter 200 value 661.160247
## iter 210 value 654.435844
## iter 220 value 647.226444
## iter 230 value 640.727026
## iter 240 value 636.886235
## iter 250 value 631.626632
## iter 260 value 624.508053
## iter 270 value 619.924054
## iter 280 value 617.545892
## iter 290 value 616.439462
## iter 300 value 614.714624
## iter 310 value 611.756548
## iter 320 value 608.852541
## iter 330 value 605.749551
## iter 340 value 601.551114
## iter 350 value 593.004595
## iter 360 value 586.882036
## iter 370 value 583.490620
## iter 380 value 581.778686
## iter 390 value 580.889623
## iter 400 value 580.289483
## iter 410 value 579.851242
## iter 420 value 579.543287
## iter 430 value 579.238153
## iter 440 value 578.992822
## iter 450 value 578.887321
## iter 460 value 578.874498
## iter 470 value 578.872196
## final  value 578.871982 
## converged
## # weights:  15
## initial  value 1419096.180400 
## iter  10 value 24243.164713
## iter  20 value 16818.767637
## iter  30 value 16783.728277
## iter  40 value 9259.528519
## iter  50 value 7749.686175
## iter  60 value 5718.652105
## iter  70 value 3114.259466
## iter  80 value 1818.432927
## iter  90 value 1786.490041
## iter 100 value 1774.681323
## iter 110 value 1767.391368
## iter 120 value 1764.334013
## iter 130 value 1761.385886
## final  value 1761.294808 
## converged
## # weights:  36
## initial  value 1429100.960345 
## iter  10 value 24465.631986
## iter  20 value 17605.222419
## iter  30 value 10533.261836
## iter  40 value 4307.695702
## iter  50 value 1662.748767
## iter  60 value 1247.991988
## iter  70 value 1147.915670
## iter  80 value 1125.887587
## iter  90 value 1114.808595
## iter 100 value 1104.140790
## iter 110 value 1087.812745
## iter 120 value 1072.520031
## iter 130 value 1052.024077
## iter 140 value 1040.765059
## iter 150 value 1030.494747
## iter 160 value 1028.937603
## iter 170 value 1021.338011
## iter 180 value 1013.486271
## iter 190 value 1004.193710
## iter 200 value 988.909341
## iter 210 value 973.125634
## iter 220 value 962.630823
## iter 230 value 962.094535
## iter 240 value 960.885653
## iter 250 value 959.483035
## iter 260 value 955.910934
## iter 270 value 954.679085
## iter 280 value 954.069128
## iter 290 value 953.736815
## iter 300 value 953.624310
## iter 310 value 953.482168
## iter 320 value 952.966266
## iter 330 value 952.093411
## iter 340 value 951.415431
## iter 350 value 950.911766
## iter 360 value 950.816826
## iter 370 value 950.769063
## iter 380 value 950.766590
## iter 390 value 950.751871
## iter 400 value 950.740473
## iter 410 value 950.727509
## iter 410 value 950.727508
## iter 410 value 950.727508
## final  value 950.727508 
## converged
## # weights:  71
## initial  value 1401712.653461 
## iter  10 value 1738.568841
## iter  20 value 1155.969694
## iter  30 value 1066.155055
## iter  40 value 994.421390
## iter  50 value 926.207042
## iter  60 value 885.460370
## iter  70 value 846.641929
## iter  80 value 785.768292
## iter  90 value 761.296563
## iter 100 value 745.013847
## iter 110 value 735.893526
## iter 120 value 726.662173
## iter 130 value 711.057169
## iter 140 value 696.898191
## iter 150 value 691.769244
## iter 160 value 688.209711
## iter 170 value 684.314315
## iter 180 value 677.004626
## iter 190 value 654.829775
## iter 200 value 620.104902
## iter 210 value 606.666531
## iter 220 value 598.862752
## iter 230 value 593.971127
## iter 240 value 587.916606
## iter 250 value 585.268804
## iter 260 value 582.687583
## iter 270 value 581.012096
## iter 280 value 579.174268
## iter 290 value 576.781503
## iter 300 value 576.419454
## iter 310 value 575.270715
## iter 320 value 572.051387
## iter 330 value 570.105102
## iter 340 value 568.706047
## iter 350 value 562.401978
## iter 360 value 541.358945
## iter 370 value 537.930960
## iter 380 value 534.613389
## iter 390 value 533.733992
## iter 400 value 532.607633
## iter 410 value 532.221327
## iter 420 value 532.085708
## iter 430 value 530.919608
## iter 440 value 524.509179
## iter 450 value 519.374898
## iter 460 value 518.269591
## iter 470 value 517.068506
## iter 480 value 516.304350
## iter 490 value 516.230214
## iter 500 value 516.227264
## final  value 516.227264 
## stopped after 500 iterations
## # weights:  106
## initial  value 1387542.547875 
## iter  10 value 2235.721775
## iter  20 value 1219.681810
## iter  30 value 926.131440
## iter  40 value 765.195906
## iter  50 value 690.558284
## iter  60 value 634.275832
## iter  70 value 592.590454
## iter  80 value 547.540247
## iter  90 value 517.199684
## iter 100 value 493.515042
## iter 110 value 479.050776
## iter 120 value 470.329222
## iter 130 value 463.030730
## iter 140 value 449.715226
## iter 150 value 437.567532
## iter 160 value 432.195300
## iter 170 value 427.655493
## iter 180 value 425.090481
## iter 190 value 422.483890
## iter 200 value 419.598959
## iter 210 value 417.697275
## iter 220 value 416.829767
## iter 230 value 416.463387
## iter 240 value 416.068524
## iter 250 value 415.563033
## iter 260 value 414.463444
## iter 270 value 412.521118
## iter 280 value 410.971977
## iter 290 value 409.564001
## iter 300 value 408.638900
## iter 310 value 407.829637
## iter 320 value 407.066443
## iter 330 value 407.027775
## iter 340 value 407.015843
## iter 350 value 407.008033
## iter 360 value 407.006771
## iter 370 value 407.006464
## final  value 407.006419 
## converged
## # weights:  141
## initial  value 1402239.465710 
## iter  10 value 3053.206236
## iter  20 value 1196.290006
## iter  30 value 926.935640
## iter  40 value 767.047447
## iter  50 value 682.034414
## iter  60 value 627.714926
## iter  70 value 560.983523
## iter  80 value 508.656911
## iter  90 value 473.707737
## iter 100 value 446.345624
## iter 110 value 426.729946
## iter 120 value 411.078464
## iter 130 value 392.680100
## iter 140 value 370.224893
## iter 150 value 337.476501
## iter 160 value 307.837431
## iter 170 value 299.046501
## iter 180 value 295.389558
## iter 190 value 291.701766
## iter 200 value 285.363260
## iter 210 value 275.058940
## iter 220 value 262.991061
## iter 230 value 256.318150
## iter 240 value 252.021116
## iter 250 value 245.599478
## iter 260 value 243.138813
## iter 270 value 241.202795
## iter 280 value 239.584831
## iter 290 value 238.314803
## iter 300 value 237.212042
## iter 310 value 235.419781
## iter 320 value 233.273965
## iter 330 value 231.424120
## iter 340 value 228.110930
## iter 350 value 224.330065
## iter 360 value 220.771646
## iter 370 value 217.167106
## iter 380 value 212.917724
## iter 390 value 209.748084
## iter 400 value 208.313777
## iter 410 value 205.747497
## iter 420 value 203.215240
## iter 430 value 201.087043
## iter 440 value 200.084978
## iter 450 value 199.753660
## iter 460 value 199.119059
## iter 470 value 198.118019
## iter 480 value 197.129015
## iter 490 value 196.066459
## iter 500 value 195.341673
## final  value 195.341673 
## stopped after 500 iterations
## # weights:  15
## initial  value 1412281.169006 
## iter  10 value 6731.743776
## iter  20 value 6379.033364
## iter  30 value 5647.390901
## iter  40 value 5633.300355
## iter  50 value 5577.514875
## iter  60 value 5551.306084
## iter  70 value 4977.434185
## iter  80 value 2776.380282
## iter  90 value 1932.744927
## iter 100 value 1887.601266
## iter 110 value 1847.979838
## iter 120 value 1835.088293
## iter 130 value 1833.105497
## iter 140 value 1828.924685
## iter 150 value 1826.369446
## iter 160 value 1825.722543
## final  value 1825.720895 
## converged
## # weights:  36
## initial  value 1390095.098143 
## iter  10 value 5041.909548
## iter  20 value 2366.320459
## iter  30 value 1839.238992
## iter  40 value 1707.588729
## iter  50 value 1679.127363
## iter  60 value 1618.661903
## iter  70 value 1484.753328
## iter  80 value 1392.180254
## iter  90 value 1299.002627
## iter 100 value 1268.296648
## iter 110 value 1244.937003
## iter 120 value 1147.495392
## iter 130 value 1124.845607
## iter 140 value 1120.836930
## iter 150 value 1118.772810
## iter 160 value 1118.163975
## iter 170 value 1117.728992
## iter 180 value 1117.453079
## iter 190 value 1116.382995
## iter 200 value 1116.174988
## iter 210 value 1115.945301
## iter 220 value 1115.726556
## iter 230 value 1115.629768
## iter 240 value 1115.568923
## iter 250 value 1115.566601
## iter 260 value 1115.303561
## iter 270 value 1114.941393
## iter 280 value 1113.969025
## iter 290 value 1112.774834
## iter 300 value 1103.992224
## iter 310 value 1089.450516
## iter 320 value 1083.596173
## iter 330 value 1080.955721
## iter 340 value 1080.601806
## iter 350 value 1077.281110
## iter 360 value 1075.715071
## iter 370 value 1075.612399
## iter 380 value 1074.594064
## iter 390 value 1073.595974
## iter 400 value 1073.333542
## iter 410 value 1072.920142
## iter 420 value 1072.410112
## iter 430 value 1072.237846
## iter 440 value 1072.234172
## iter 450 value 1072.214131
## iter 460 value 1071.926730
## iter 470 value 1071.807091
## iter 480 value 1071.776645
## iter 490 value 1071.749801
## iter 500 value 1071.711436
## final  value 1071.711436 
## stopped after 500 iterations
## # weights:  71
## initial  value 1410171.270412 
## iter  10 value 3922.442191
## iter  20 value 1845.385299
## iter  30 value 1367.609950
## iter  40 value 1235.177604
## iter  50 value 1124.674640
## iter  60 value 1036.832182
## iter  70 value 965.602156
## iter  80 value 930.183095
## iter  90 value 903.169432
## iter 100 value 883.843379
## iter 110 value 868.816377
## iter 120 value 858.561122
## iter 130 value 841.498351
## iter 140 value 834.071478
## iter 150 value 832.656036
## iter 160 value 832.305317
## iter 170 value 829.076600
## iter 180 value 825.246079
## iter 190 value 814.529060
## iter 200 value 803.113719
## iter 210 value 793.630889
## iter 220 value 789.577656
## iter 230 value 786.744274
## iter 240 value 784.402387
## iter 250 value 782.373796
## iter 260 value 781.749209
## iter 270 value 781.124557
## iter 280 value 779.764384
## iter 290 value 775.887748
## iter 300 value 775.236538
## iter 310 value 774.331260
## iter 320 value 773.657741
## iter 330 value 773.232376
## iter 340 value 771.625118
## iter 350 value 769.012867
## iter 360 value 765.343257
## iter 370 value 764.266088
## iter 380 value 763.993307
## iter 390 value 763.806990
## iter 400 value 763.574420
## iter 410 value 763.354519
## iter 420 value 763.202166
## iter 430 value 763.118776
## iter 440 value 763.105596
## iter 450 value 763.079853
## iter 460 value 762.924561
## iter 470 value 761.849135
## iter 480 value 758.911857
## iter 490 value 756.850933
## iter 500 value 756.137036
## final  value 756.137036 
## stopped after 500 iterations
## # weights:  106
## initial  value 1478360.623413 
## iter  10 value 1332.081732
## iter  20 value 1074.661955
## iter  30 value 948.322799
## iter  40 value 847.519714
## iter  50 value 790.092241
## iter  60 value 729.444990
## iter  70 value 663.083422
## iter  80 value 615.765612
## iter  90 value 588.464522
## iter 100 value 574.147784
## iter 110 value 550.918580
## iter 120 value 523.963058
## iter 130 value 500.123274
## iter 140 value 484.232037
## iter 150 value 462.144147
## iter 160 value 442.590446
## iter 170 value 430.225762
## iter 180 value 414.627835
## iter 190 value 403.766862
## iter 200 value 391.667554
## iter 210 value 387.145165
## iter 220 value 385.642265
## iter 230 value 384.809830
## iter 240 value 383.404023
## iter 250 value 380.527158
## iter 260 value 375.730019
## iter 270 value 367.811281
## iter 280 value 360.458502
## iter 290 value 351.362135
## iter 300 value 342.886314
## iter 310 value 336.106750
## iter 320 value 327.551536
## iter 330 value 317.230931
## iter 340 value 309.049753
## iter 350 value 305.764144
## iter 360 value 304.684988
## iter 370 value 303.860663
## iter 380 value 302.630532
## iter 390 value 301.785246
## iter 400 value 301.415839
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## iter 420 value 300.972762
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## iter 450 value 300.840149
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## iter 470 value 300.794936
## iter 480 value 300.767890
## iter 490 value 300.731218
## iter 500 value 300.704849
## final  value 300.704849 
## stopped after 500 iterations
## # weights:  141
## initial  value 1485286.129457 
## iter  10 value 1403.251952
## iter  20 value 1034.048557
## iter  30 value 906.531608
## iter  40 value 828.173311
## iter  50 value 753.118025
## iter  60 value 689.152492
## iter  70 value 646.366669
## iter  80 value 602.039234
## iter  90 value 559.905052
## iter 100 value 511.450492
## iter 110 value 464.840846
## iter 120 value 439.666439
## iter 130 value 417.329673
## iter 140 value 397.630989
## iter 150 value 388.131968
## iter 160 value 376.564218
## iter 170 value 367.829786
## iter 180 value 356.070019
## iter 190 value 343.605139
## iter 200 value 329.143077
## iter 210 value 317.602004
## iter 220 value 310.053088
## iter 230 value 300.796864
## iter 240 value 294.137987
## iter 250 value 282.768872
## iter 260 value 277.202262
## iter 270 value 273.950637
## iter 280 value 266.753687
## iter 290 value 261.536814
## iter 300 value 259.928962
## iter 310 value 257.704416
## iter 320 value 255.228433
## iter 330 value 252.829330
## iter 340 value 249.950407
## iter 350 value 244.149118
## iter 360 value 238.863935
## iter 370 value 234.341308
## iter 380 value 227.984255
## iter 390 value 219.850093
## iter 400 value 212.599590
## iter 410 value 206.558692
## iter 420 value 203.982743
## iter 430 value 199.481753
## iter 440 value 196.502434
## iter 450 value 195.118177
## iter 460 value 194.367560
## iter 470 value 193.021333
## iter 480 value 191.509376
## iter 490 value 191.246782
## iter 500 value 190.922837
## final  value 190.922837 
## stopped after 500 iterations
## # weights:  15
## initial  value 1393833.087880 
## iter  10 value 9255.145350
## iter  20 value 6964.060991
## iter  30 value 5154.555610
## iter  40 value 4096.000374
## iter  50 value 3383.560369
## iter  60 value 2212.645230
## iter  70 value 1630.596245
## iter  80 value 1483.702580
## iter  90 value 1262.396080
## iter 100 value 1195.805107
## iter 110 value 1191.271052
## iter 120 value 1175.860179
## iter 130 value 1169.139250
## iter 140 value 1168.833853
## iter 150 value 1164.432615
## iter 160 value 1162.360653
## iter 170 value 1162.193309
## iter 180 value 1161.026879
## iter 190 value 1159.804129
## iter 200 value 1159.615471
## iter 210 value 1159.216867
## iter 220 value 1158.786125
## iter 230 value 1158.722283
## iter 240 value 1158.393469
## iter 250 value 1158.254385
## iter 260 value 1158.118151
## iter 270 value 1158.025459
## iter 280 value 1157.880653
## iter 290 value 1157.720970
## iter 300 value 1157.718826
## final  value 1157.718716 
## converged
## # weights:  36
## initial  value 1436950.324028 
## iter  10 value 37696.048330
## iter  20 value 22008.779316
## iter  30 value 7376.174966
## iter  40 value 4050.017833
## iter  50 value 2071.501161
## iter  60 value 1723.824610
## iter  70 value 1500.510096
## iter  80 value 1451.071267
## iter  90 value 1386.689232
## iter 100 value 1237.043663
## iter 110 value 1136.803726
## iter 120 value 1118.050103
## iter 130 value 1103.988324
## iter 140 value 1083.786702
## iter 150 value 1065.434774
## iter 160 value 1061.956556
## iter 170 value 1054.231372
## iter 180 value 1046.067425
## iter 190 value 1043.519932
## iter 200 value 1039.888204
## iter 210 value 1037.693830
## iter 220 value 1034.989921
## iter 230 value 1034.661807
## iter 240 value 1034.310702
## iter 250 value 1025.715255
## iter 260 value 1018.731726
## iter 270 value 1015.602075
## iter 280 value 1014.959221
## iter 290 value 1014.306198
## iter 300 value 1014.218162
## iter 310 value 1014.125936
## iter 320 value 1013.614775
## iter 330 value 1013.410692
## iter 340 value 1013.092562
## iter 350 value 1012.915281
## iter 360 value 1012.844053
## iter 370 value 1012.764815
## iter 380 value 1012.755096
## iter 390 value 1012.667675
## iter 400 value 1012.645304
## iter 410 value 1012.513499
## iter 420 value 1012.343160
## iter 430 value 1012.197155
## iter 440 value 1012.081758
## iter 450 value 1012.059322
## iter 460 value 1011.995206
## iter 470 value 1011.634320
## iter 480 value 1011.560636
## iter 490 value 1011.486943
## iter 500 value 1011.448402
## final  value 1011.448402 
## stopped after 500 iterations
## # weights:  71
## initial  value 1373795.183354 
## iter  10 value 1467.671411
## iter  20 value 1190.361563
## iter  30 value 1058.369983
## iter  40 value 973.855350
## iter  50 value 931.853458
## iter  60 value 910.870881
## iter  70 value 886.244227
## iter  80 value 867.873660
## iter  90 value 851.005555
## iter 100 value 817.233794
## iter 110 value 772.686890
## iter 120 value 749.218785
## iter 130 value 735.352059
## iter 140 value 716.572771
## iter 150 value 699.188656
## iter 160 value 690.381305
## iter 170 value 678.435008
## iter 180 value 660.602861
## iter 190 value 635.974079
## iter 200 value 613.493874
## iter 210 value 602.539371
## iter 220 value 599.064054
## iter 230 value 598.211614
## iter 240 value 596.578873
## iter 250 value 594.386557
## iter 260 value 590.606488
## iter 270 value 589.295301
## iter 280 value 589.041488
## iter 290 value 584.391163
## iter 300 value 578.015877
## iter 310 value 572.985175
## iter 320 value 570.728695
## iter 330 value 570.215283
## iter 340 value 567.625708
## iter 350 value 563.061997
## iter 360 value 560.135472
## iter 370 value 560.100963
## iter 380 value 559.979215
## iter 390 value 559.412550
## iter 400 value 559.091334
## iter 410 value 559.065398
## iter 420 value 559.062563
## iter 430 value 559.061609
## iter 440 value 559.060387
## iter 450 value 559.059577
## iter 460 value 559.057772
## iter 470 value 559.030101
## iter 480 value 559.010226
## iter 490 value 558.869629
## iter 500 value 558.867047
## final  value 558.867047 
## stopped after 500 iterations
## # weights:  106
## initial  value 1422508.792575 
## iter  10 value 1604.229250
## iter  20 value 1144.741412
## iter  30 value 1023.547140
## iter  40 value 847.057535
## iter  50 value 762.110977
## iter  60 value 722.613225
## iter  70 value 659.191233
## iter  80 value 599.539229
## iter  90 value 562.358290
## iter 100 value 530.829342
## iter 110 value 503.809161
## iter 120 value 484.532128
## iter 130 value 472.285318
## iter 140 value 455.175128
## iter 150 value 441.172852
## iter 160 value 428.221210
## iter 170 value 417.075854
## iter 180 value 405.347577
## iter 190 value 387.174953
## iter 200 value 365.246886
## iter 210 value 352.059141
## iter 220 value 345.813112
## iter 230 value 343.433402
## iter 240 value 341.587657
## iter 250 value 337.439209
## iter 260 value 334.009899
## iter 270 value 332.205157
## iter 280 value 328.075796
## iter 290 value 318.789430
## iter 300 value 313.849969
## iter 310 value 310.504834
## iter 320 value 309.254215
## iter 330 value 307.750742
## iter 340 value 307.182611
## iter 350 value 306.800059
## iter 360 value 306.242161
## iter 370 value 305.526966
## iter 380 value 305.273011
## iter 390 value 305.032022
## iter 400 value 304.876989
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## iter 450 value 304.562718
## iter 460 value 304.550529
## iter 470 value 304.538611
## iter 480 value 304.516555
## iter 490 value 304.451126
## iter 500 value 304.374046
## final  value 304.374046 
## stopped after 500 iterations
## # weights:  141
## initial  value 1419651.917360 
## iter  10 value 3742.707389
## iter  20 value 1844.516600
## iter  30 value 1233.778202
## iter  40 value 924.647094
## iter  50 value 816.118792
## iter  60 value 745.861086
## iter  70 value 686.694500
## iter  80 value 594.468171
## iter  90 value 549.466363
## iter 100 value 521.653829
## iter 110 value 504.165182
## iter 120 value 493.109285
## iter 130 value 483.802735
## iter 140 value 472.122369
## iter 150 value 455.490005
## iter 160 value 441.446178
## iter 170 value 433.127112
## iter 180 value 425.325851
## iter 190 value 402.123044
## iter 200 value 392.430410
## iter 210 value 387.563750
## iter 220 value 383.510923
## iter 230 value 380.680133
## iter 240 value 377.018863
## iter 250 value 373.520346
## iter 260 value 370.286429
## iter 270 value 367.760668
## iter 280 value 366.022591
## iter 290 value 364.951206
## iter 300 value 364.529219
## iter 310 value 364.072646
## iter 320 value 362.820390
## iter 330 value 361.037193
## iter 340 value 357.088143
## iter 350 value 353.362841
## iter 360 value 348.184167
## iter 370 value 343.094806
## iter 380 value 338.114852
## iter 390 value 333.439967
## iter 400 value 330.706842
## iter 410 value 328.623617
## iter 420 value 326.142137
## iter 430 value 323.642450
## iter 440 value 321.360963
## iter 450 value 318.926690
## iter 460 value 317.549878
## iter 470 value 316.661972
## iter 480 value 315.690607
## iter 490 value 314.368311
## iter 500 value 313.486442
## final  value 313.486442 
## stopped after 500 iterations
## # weights:  15
## initial  value 1428285.941801 
## iter  10 value 10367.997099
## iter  20 value 5384.233863
## iter  30 value 4733.764398
## iter  40 value 4179.718259
## iter  50 value 3515.064505
## iter  60 value 2490.210590
## iter  70 value 1695.148651
## iter  80 value 1440.748098
## iter  90 value 1439.260211
## iter 100 value 1402.164175
## iter 110 value 1390.301325
## iter 120 value 1388.883047
## iter 130 value 1385.512091
## iter 140 value 1380.777015
## iter 150 value 1380.254933
## iter 160 value 1379.884817
## iter 170 value 1377.159901
## iter 180 value 1376.390297
## iter 190 value 1376.321551
## iter 200 value 1375.136304
## iter 210 value 1374.761578
## iter 220 value 1374.754498
## iter 230 value 1374.669136
## iter 240 value 1373.777081
## iter 250 value 1373.742389
## iter 260 value 1373.737753
## iter 270 value 1373.455007
## iter 280 value 1373.156505
## iter 290 value 1373.152758
## iter 300 value 1373.050846
## iter 310 value 1372.676770
## iter 320 value 1372.667689
## final  value 1372.666981 
## converged
## # weights:  36
## initial  value 1416048.944683 
## iter  10 value 33595.315372
## iter  20 value 11697.134805
## iter  30 value 7586.798075
## iter  40 value 4967.783918
## iter  50 value 2609.581764
## iter  60 value 1764.424172
## iter  70 value 1424.076549
## iter  80 value 1416.168105
## iter  90 value 1413.657843
## iter 100 value 1391.616466
## iter 110 value 1385.197236
## iter 120 value 1372.459027
## iter 130 value 1365.952730
## iter 140 value 1359.989422
## iter 150 value 1356.785347
## iter 160 value 1353.201929
## iter 170 value 1349.859029
## iter 180 value 1343.542688
## iter 190 value 1312.963527
## iter 200 value 1284.163019
## iter 210 value 1252.490890
## iter 220 value 1232.840004
## iter 230 value 1164.362629
## iter 240 value 1064.279027
## iter 250 value 1002.627750
## iter 260 value 990.943353
## iter 270 value 988.137871
## iter 280 value 986.478458
## iter 290 value 985.087208
## iter 300 value 979.935588
## iter 310 value 975.781223
## iter 320 value 971.956924
## iter 330 value 968.012112
## iter 340 value 966.202190
## iter 350 value 965.728155
## iter 360 value 965.693988
## iter 370 value 965.497300
## iter 380 value 965.373751
## iter 390 value 965.273104
## iter 400 value 965.135688
## iter 410 value 964.956750
## iter 420 value 964.855489
## iter 430 value 964.810530
## iter 440 value 964.333657
## iter 450 value 961.911040
## iter 460 value 961.858375
## iter 470 value 961.402432
## iter 480 value 960.952871
## iter 490 value 960.900232
## iter 500 value 960.884811
## final  value 960.884811 
## stopped after 500 iterations
## # weights:  71
## initial  value 1423448.721199 
## iter  10 value 3356.214228
## iter  20 value 1436.194580
## iter  30 value 1115.659147
## iter  40 value 993.275726
## iter  50 value 896.233405
## iter  60 value 854.386689
## iter  70 value 789.048796
## iter  80 value 715.556647
## iter  90 value 677.847337
## iter 100 value 662.108900
## iter 110 value 648.741383
## iter 120 value 635.352515
## iter 130 value 627.995084
## iter 140 value 624.663383
## iter 150 value 622.988015
## iter 160 value 622.451509
## iter 170 value 621.889932
## iter 180 value 620.366400
## iter 190 value 618.109861
## iter 200 value 608.176236
## iter 210 value 595.586930
## iter 220 value 578.130884
## iter 230 value 570.324218
## iter 240 value 561.531646
## iter 250 value 540.408310
## iter 260 value 534.732373
## iter 270 value 531.233066
## iter 280 value 529.224991
## iter 290 value 527.893352
## iter 300 value 527.519890
## iter 310 value 527.280860
## iter 320 value 526.790157
## iter 330 value 525.949117
## iter 340 value 525.798255
## iter 350 value 525.530853
## iter 360 value 524.204819
## iter 370 value 520.281685
## iter 380 value 518.079267
## iter 390 value 517.085005
## iter 400 value 517.069058
## iter 410 value 517.057983
## iter 420 value 517.049201
## iter 430 value 516.946065
## iter 440 value 516.898517
## final  value 516.898413 
## converged
## # weights:  106
## initial  value 1431069.838444 
## iter  10 value 1605.362992
## iter  20 value 1038.472955
## iter  30 value 889.850918
## iter  40 value 810.627389
## iter  50 value 765.594627
## iter  60 value 731.579156
## iter  70 value 677.401983
## iter  80 value 642.761799
## iter  90 value 610.444947
## iter 100 value 598.230680
## iter 110 value 586.031659
## iter 120 value 552.625311
## iter 130 value 525.534414
## iter 140 value 505.930038
## iter 150 value 484.843288
## iter 160 value 470.944617
## iter 170 value 457.764296
## iter 180 value 444.370626
## iter 190 value 427.927605
## iter 200 value 411.586044
## iter 210 value 402.896423
## iter 220 value 400.061325
## iter 230 value 398.425777
## iter 240 value 396.308668
## iter 250 value 393.793231
## iter 260 value 391.135145
## iter 270 value 387.873003
## iter 280 value 385.137908
## iter 290 value 379.031102
## iter 300 value 368.587815
## iter 310 value 360.802257
## iter 320 value 356.824018
## iter 330 value 353.533179
## iter 340 value 348.740488
## iter 350 value 342.562836
## iter 360 value 333.505736
## iter 370 value 324.350102
## iter 380 value 320.586783
## iter 390 value 318.125385
## iter 400 value 315.847213
## iter 410 value 314.314988
## iter 420 value 312.608369
## iter 430 value 312.000039
## iter 440 value 311.964930
## iter 450 value 311.896058
## iter 460 value 311.781143
## iter 470 value 311.598088
## iter 480 value 311.019901
## iter 490 value 310.304530
## iter 500 value 308.603216
## final  value 308.603216 
## stopped after 500 iterations
## # weights:  141
## initial  value 1415086.305566 
## iter  10 value 1539.025169
## iter  20 value 1073.999351
## iter  30 value 895.746897
## iter  40 value 802.385289
## iter  50 value 714.426882
## iter  60 value 643.529536
## iter  70 value 585.430611
## iter  80 value 547.714703
## iter  90 value 517.931982
## iter 100 value 493.573022
## iter 110 value 473.072988
## iter 120 value 456.125464
## iter 130 value 436.941000
## iter 140 value 411.679932
## iter 150 value 391.803946
## iter 160 value 369.232702
## iter 170 value 348.776458
## iter 180 value 333.787354
## iter 190 value 323.835368
## iter 200 value 312.685920
## iter 210 value 303.662047
## iter 220 value 296.109205
## iter 230 value 290.121973
## iter 240 value 285.850570
## iter 250 value 282.538825
## iter 260 value 280.079597
## iter 270 value 276.903926
## iter 280 value 274.047946
## iter 290 value 272.483554
## iter 300 value 271.585217
## iter 310 value 269.184889
## iter 320 value 266.877628
## iter 330 value 263.078652
## iter 340 value 260.688584
## iter 350 value 258.228147
## iter 360 value 254.034513
## iter 370 value 250.502413
## iter 380 value 247.625926
## iter 390 value 244.496228
## iter 400 value 241.348157
## iter 410 value 239.246278
## iter 420 value 237.087519
## iter 430 value 236.107795
## iter 440 value 234.753297
## iter 450 value 232.660902
## iter 460 value 229.205451
## iter 470 value 226.515100
## iter 480 value 224.522724
## iter 490 value 222.747790
## iter 500 value 221.469696
## final  value 221.469696 
## stopped after 500 iterations
## # weights:  15
## initial  value 1394295.921982 
## iter  10 value 15725.178140
## iter  20 value 6950.118426
## iter  30 value 3527.563316
## iter  40 value 2926.561664
## iter  50 value 2082.149682
## iter  60 value 1945.248369
## iter  70 value 1726.936931
## iter  80 value 1626.058896
## iter  90 value 1600.555688
## iter 100 value 1491.584325
## iter 110 value 1430.487849
## iter 120 value 1426.914361
## iter 130 value 1417.844012
## iter 140 value 1417.608921
## final  value 1417.605953 
## converged
## # weights:  36
## initial  value 1347134.598101 
## iter  10 value 11028.784269
## iter  20 value 8797.130275
## iter  30 value 6663.104295
## iter  40 value 5565.344710
## iter  50 value 4672.154940
## iter  60 value 2762.743496
## iter  70 value 1907.016013
## iter  80 value 1510.882325
## iter  90 value 1352.205699
## iter 100 value 1242.162793
## iter 110 value 1140.369818
## iter 120 value 1114.828247
## iter 130 value 1093.160072
## iter 140 value 1089.654473
## iter 150 value 1081.250957
## iter 160 value 1074.681742
## iter 170 value 1068.314820
## iter 180 value 1060.152377
## iter 190 value 1057.823395
## iter 200 value 1054.824459
## iter 210 value 1054.120271
## iter 220 value 1053.890553
## iter 230 value 1053.668824
## iter 240 value 1053.626440
## iter 240 value 1053.626435
## iter 240 value 1053.626435
## final  value 1053.626435 
## converged
## # weights:  71
## initial  value 1407159.361970 
## iter  10 value 2080.707622
## iter  20 value 1222.959048
## iter  30 value 1139.031292
## iter  40 value 1088.840644
## iter  50 value 1025.596729
## iter  60 value 992.682563
## iter  70 value 957.366108
## iter  80 value 947.218257
## iter  90 value 941.400262
## iter 100 value 932.749781
## iter 110 value 923.724345
## iter 120 value 922.083410
## iter 130 value 920.543013
## iter 140 value 918.743045
## iter 150 value 916.149909
## iter 160 value 912.622761
## iter 170 value 910.312965
## iter 180 value 908.758714
## iter 190 value 907.780680
## iter 200 value 905.829374
## iter 210 value 901.436797
## iter 220 value 899.730391
## iter 230 value 895.945485
## iter 240 value 892.002352
## iter 250 value 887.832319
## iter 260 value 883.059465
## iter 270 value 881.037156
## iter 280 value 880.369143
## iter 290 value 880.030669
## iter 300 value 879.997807
## iter 310 value 879.975875
## iter 320 value 879.961292
## iter 330 value 879.956959
## iter 340 value 879.955164
## final  value 879.955030 
## converged
## # weights:  106
## initial  value 1486846.061428 
## iter  10 value 2428.037016
## iter  20 value 1507.325134
## iter  30 value 1305.461535
## iter  40 value 1176.980462
## iter  50 value 1059.562469
## iter  60 value 999.999014
## iter  70 value 963.302151
## iter  80 value 933.666888
## iter  90 value 911.743282
## iter 100 value 894.599268
## iter 110 value 866.311955
## iter 120 value 851.952594
## iter 130 value 846.041335
## iter 140 value 837.459804
## iter 150 value 832.177343
## iter 160 value 828.741008
## iter 170 value 816.753892
## iter 180 value 802.135221
## iter 190 value 794.943347
## iter 200 value 779.809614
## iter 210 value 764.516860
## iter 220 value 749.593789
## iter 230 value 742.031569
## iter 240 value 734.787252
## iter 250 value 731.933168
## iter 260 value 730.611367
## iter 270 value 730.395485
## iter 280 value 730.305644
## iter 290 value 730.219317
## iter 300 value 729.277474
## iter 310 value 717.448151
## iter 320 value 695.859331
## iter 330 value 682.371751
## iter 340 value 675.831175
## iter 350 value 673.195289
## iter 360 value 670.744424
## iter 370 value 668.743232
## iter 380 value 667.929451
## iter 390 value 667.336008
## iter 400 value 666.732642
## iter 410 value 666.524212
## iter 420 value 666.483712
## iter 430 value 666.469360
## iter 440 value 666.467182
## iter 450 value 666.463400
## iter 460 value 666.458956
## iter 470 value 666.424632
## iter 480 value 666.310307
## iter 490 value 666.219338
## iter 500 value 666.188280
## final  value 666.188280 
## stopped after 500 iterations
## # weights:  141
## initial  value 1455583.108312 
## iter  10 value 2012.268587
## iter  20 value 1219.817389
## iter  30 value 1036.002579
## iter  40 value 926.844960
## iter  50 value 860.576797
## iter  60 value 799.217322
## iter  70 value 747.281953
## iter  80 value 725.615849
## iter  90 value 713.155563
## iter 100 value 701.083016
## iter 110 value 687.648890
## iter 120 value 677.301906
## iter 130 value 667.633994
## iter 140 value 655.099693
## iter 150 value 633.583338
## iter 160 value 618.453989
## iter 170 value 604.189942
## iter 180 value 589.909041
## iter 190 value 582.114882
## iter 200 value 577.626129
## iter 210 value 572.628799
## iter 220 value 570.313465
## iter 230 value 568.502208
## iter 240 value 567.216887
## iter 250 value 566.071282
## iter 260 value 565.451764
## iter 270 value 565.025617
## iter 280 value 564.733917
## iter 290 value 564.320006
## iter 300 value 563.493828
## iter 310 value 561.614624
## iter 320 value 559.379847
## iter 330 value 557.426177
## iter 340 value 555.927147
## iter 350 value 554.204455
## iter 360 value 551.230347
## iter 370 value 547.665028
## iter 380 value 545.503561
## iter 390 value 544.376962
## iter 400 value 543.851102
## iter 410 value 543.657720
## iter 420 value 543.588437
## iter 430 value 543.557833
## iter 440 value 543.554129
## final  value 543.554103 
## converged
## # weights:  15
## initial  value 1421575.281458 
## iter  10 value 23729.334805
## iter  20 value 5960.228677
## iter  30 value 4062.201322
## iter  40 value 3330.827421
## iter  50 value 2292.319589
## iter  60 value 1666.032762
## iter  70 value 1408.885441
## iter  80 value 1361.494750
## iter  90 value 1211.854616
## iter 100 value 1171.792701
## iter 110 value 1169.600978
## iter 120 value 1161.663768
## iter 130 value 1155.590907
## iter 140 value 1154.957299
## iter 150 value 1150.924073
## iter 160 value 1144.617864
## iter 170 value 1143.256593
## iter 180 value 1140.571872
## iter 190 value 1139.691216
## iter 200 value 1139.288432
## iter 210 value 1138.383071
## iter 220 value 1137.841924
## iter 230 value 1137.772143
## final  value 1137.772117 
## converged
## # weights:  36
## initial  value 1381766.120345 
## iter  10 value 58309.046647
## iter  20 value 24699.633022
## iter  30 value 4995.195262
## iter  40 value 3354.017372
## iter  50 value 2117.263207
## iter  60 value 1637.241474
## iter  70 value 1628.090930
## iter  80 value 1596.015705
## iter  90 value 1577.414122
## iter 100 value 1544.711459
## iter 110 value 1499.878531
## iter 120 value 1456.151384
## iter 130 value 1412.051719
## iter 140 value 1397.126111
## iter 150 value 1330.213821
## iter 160 value 1238.856298
## iter 170 value 1125.587019
## iter 180 value 1114.735682
## iter 190 value 1111.887815
## iter 200 value 1106.885945
## iter 210 value 1095.507826
## iter 220 value 1053.777491
## iter 230 value 1045.287486
## iter 240 value 1027.479572
## iter 250 value 991.520845
## iter 260 value 982.516983
## iter 270 value 979.950008
## iter 280 value 977.760914
## iter 290 value 976.924834
## iter 300 value 976.847138
## iter 310 value 976.164178
## iter 320 value 975.700124
## iter 330 value 975.273489
## iter 340 value 975.230589
## iter 350 value 975.223833
## final  value 975.221511 
## converged
## # weights:  71
## initial  value 1423539.659459 
## iter  10 value 1315.407119
## iter  20 value 1119.872470
## iter  30 value 1040.580066
## iter  40 value 941.801030
## iter  50 value 867.339600
## iter  60 value 797.694983
## iter  70 value 773.720546
## iter  80 value 759.437874
## iter  90 value 744.004492
## iter 100 value 709.861428
## iter 110 value 672.383373
## iter 120 value 652.237052
## iter 130 value 643.158000
## iter 140 value 632.523104
## iter 150 value 628.512726
## iter 160 value 626.310259
## iter 170 value 620.812824
## iter 180 value 617.086361
## iter 190 value 614.386084
## iter 200 value 611.584964
## iter 210 value 608.066207
## iter 220 value 599.674097
## iter 230 value 587.187145
## iter 240 value 579.532699
## iter 250 value 576.012792
## iter 260 value 574.392626
## iter 270 value 570.711436
## iter 280 value 563.364715
## iter 290 value 561.713256
## iter 300 value 561.616435
## iter 310 value 561.470057
## iter 320 value 561.332192
## iter 330 value 561.203690
## iter 340 value 561.056547
## iter 350 value 559.451447
## iter 360 value 547.223586
## iter 370 value 534.521349
## iter 380 value 527.792883
## iter 390 value 526.873359
## iter 400 value 526.542555
## iter 410 value 526.481222
## iter 420 value 526.445360
## iter 430 value 526.436523
## iter 440 value 526.436028
## final  value 526.435531 
## converged
## # weights:  106
## initial  value 1385946.694552 
## iter  10 value 1283.998820
## iter  20 value 964.161015
## iter  30 value 864.904072
## iter  40 value 774.064934
## iter  50 value 720.915858
## iter  60 value 682.739131
## iter  70 value 646.348118
## iter  80 value 598.892153
## iter  90 value 569.273105
## iter 100 value 544.770145
## iter 110 value 524.881710
## iter 120 value 504.345923
## iter 130 value 489.064650
## iter 140 value 478.040632
## iter 150 value 465.109342
## iter 160 value 449.925572
## iter 170 value 435.114422
## iter 180 value 421.232060
## iter 190 value 413.849897
## iter 200 value 408.537927
## iter 210 value 404.036039
## iter 220 value 402.072013
## iter 230 value 400.150674
## iter 240 value 397.703607
## iter 250 value 393.044459
## iter 260 value 385.469218
## iter 270 value 372.621015
## iter 280 value 362.681109
## iter 290 value 357.163551
## iter 300 value 352.397977
## iter 310 value 345.030064
## iter 320 value 338.361856
## iter 330 value 334.757936
## iter 340 value 330.877586
## iter 350 value 328.249082
## iter 360 value 326.055109
## iter 370 value 324.699534
## iter 380 value 321.792709
## iter 390 value 315.113484
## iter 400 value 311.605808
## iter 410 value 309.652467
## iter 420 value 305.964070
## iter 430 value 303.907112
## iter 440 value 303.827274
## iter 450 value 303.714376
## iter 460 value 303.517572
## iter 470 value 303.330378
## iter 480 value 302.659422
## iter 490 value 301.533068
## iter 500 value 300.691594
## final  value 300.691594 
## stopped after 500 iterations
## # weights:  141
## initial  value 1384494.474104 
## iter  10 value 1652.988125
## iter  20 value 1098.069740
## iter  30 value 909.530477
## iter  40 value 785.522430
## iter  50 value 684.955771
## iter  60 value 613.888064
## iter  70 value 569.088664
## iter  80 value 526.313456
## iter  90 value 503.294104
## iter 100 value 482.748706
## iter 110 value 456.169031
## iter 120 value 431.360694
## iter 130 value 401.683290
## iter 140 value 375.385200
## iter 150 value 355.694948
## iter 160 value 342.281825
## iter 170 value 327.152694
## iter 180 value 317.006709
## iter 190 value 304.036521
## iter 200 value 286.099453
## iter 210 value 273.025805
## iter 220 value 264.809402
## iter 230 value 257.300104
## iter 240 value 252.933665
## iter 250 value 250.299941
## iter 260 value 248.439154
## iter 270 value 247.064123
## iter 280 value 245.373208
## iter 290 value 244.405831
## iter 300 value 243.509478
## iter 310 value 241.511481
## iter 320 value 239.037404
## iter 330 value 234.286909
## iter 340 value 230.584621
## iter 350 value 226.853420
## iter 360 value 223.307497
## iter 370 value 219.637544
## iter 380 value 217.163782
## iter 390 value 214.028496
## iter 400 value 209.624619
## iter 410 value 205.385698
## iter 420 value 203.932566
## iter 430 value 203.280752
## iter 440 value 202.543652
## iter 450 value 201.599221
## iter 460 value 200.066378
## iter 470 value 199.483043
## iter 480 value 199.328437
## iter 490 value 199.250164
## iter 500 value 199.206387
## final  value 199.206387 
## stopped after 500 iterations
## # weights:  15
## initial  value 1389490.039765 
## iter  10 value 14324.688282
## iter  20 value 5876.939923
## iter  30 value 4517.780257
## iter  40 value 3059.115888
## iter  50 value 2208.336595
## iter  60 value 1676.424631
## iter  70 value 1592.749486
## iter  80 value 1575.703559
## iter  90 value 1563.508494
## iter 100 value 1558.128344
## iter 110 value 1556.667604
## iter 120 value 1555.986945
## iter 130 value 1554.746909
## iter 140 value 1553.860923
## iter 150 value 1553.856083
## final  value 1553.856003 
## converged
## # weights:  36
## initial  value 1400210.689614 
## iter  10 value 11616.858464
## iter  20 value 11380.201817
## iter  30 value 11378.880691
## iter  40 value 11365.829135
## iter  50 value 11297.145617
## iter  60 value 10939.293155
## iter  70 value 9928.053934
## iter  80 value 7624.680954
## iter  90 value 4483.206544
## iter 100 value 2321.367049
## iter 110 value 1733.572420
## iter 120 value 1697.812244
## iter 130 value 1673.129667
## iter 140 value 1633.702204
## iter 150 value 1601.383995
## iter 160 value 1599.592955
## iter 170 value 1592.770646
## iter 180 value 1584.332821
## iter 190 value 1563.978933
## iter 200 value 1466.916266
## iter 210 value 1349.980825
## iter 220 value 1250.204052
## iter 230 value 1174.970879
## iter 240 value 1100.489867
## iter 250 value 998.437449
## iter 260 value 980.413877
## iter 270 value 967.451152
## iter 280 value 955.524788
## iter 290 value 954.054883
## iter 300 value 953.365800
## iter 310 value 949.584130
## iter 320 value 946.209705
## iter 330 value 945.438851
## iter 340 value 944.011329
## iter 350 value 943.122059
## iter 360 value 942.594361
## iter 370 value 942.517112
## iter 380 value 942.304948
## iter 390 value 942.165345
## iter 400 value 941.896572
## iter 410 value 941.795510
## iter 420 value 940.207582
## iter 430 value 934.935149
## iter 440 value 926.876531
## iter 450 value 910.952736
## iter 460 value 909.738978
## iter 470 value 909.576787
## iter 480 value 909.322225
## iter 490 value 908.962058
## iter 500 value 908.960986
## final  value 908.960986 
## stopped after 500 iterations
## # weights:  71
## initial  value 1442226.048993 
## iter  10 value 1226.933238
## iter  20 value 1072.830007
## iter  30 value 995.841818
## iter  40 value 932.787010
## iter  50 value 867.421080
## iter  60 value 795.313368
## iter  70 value 739.715865
## iter  80 value 705.987135
## iter  90 value 695.743869
## iter 100 value 688.699032
## iter 110 value 677.839879
## iter 120 value 672.600083
## iter 130 value 668.551755
## iter 140 value 664.636235
## iter 150 value 661.115373
## iter 160 value 657.773261
## iter 170 value 652.088244
## iter 180 value 643.287009
## iter 190 value 635.807584
## iter 200 value 624.115497
## iter 210 value 614.957016
## iter 220 value 610.401177
## iter 230 value 608.533935
## iter 240 value 606.347678
## iter 250 value 601.839140
## iter 260 value 598.744613
## iter 270 value 596.336352
## iter 280 value 594.190099
## iter 290 value 592.567980
## iter 300 value 591.980096
## iter 310 value 591.317805
## iter 320 value 589.723147
## iter 330 value 586.489483
## iter 340 value 576.796831
## iter 350 value 552.218509
## iter 360 value 541.873961
## iter 370 value 535.825268
## iter 380 value 533.092012
## iter 390 value 529.857548
## iter 400 value 527.451420
## iter 410 value 526.830642
## iter 420 value 526.608827
## iter 430 value 526.490946
## iter 440 value 526.487232
## iter 450 value 526.485257
## iter 460 value 526.476242
## iter 470 value 526.464431
## iter 480 value 526.454168
## iter 490 value 526.439074
## iter 500 value 526.382879
## final  value 526.382879 
## stopped after 500 iterations
## # weights:  106
## initial  value 1391625.891468 
## iter  10 value 1396.734866
## iter  20 value 1094.834159
## iter  30 value 951.118649
## iter  40 value 857.573586
## iter  50 value 758.462567
## iter  60 value 724.614265
## iter  70 value 666.769211
## iter  80 value 599.710709
## iter  90 value 554.485299
## iter 100 value 531.967361
## iter 110 value 515.450617
## iter 120 value 494.677584
## iter 130 value 475.551733
## iter 140 value 455.888794
## iter 150 value 439.881061
## iter 160 value 415.063318
## iter 170 value 401.734263
## iter 180 value 389.852756
## iter 190 value 379.956923
## iter 200 value 373.362452
## iter 210 value 360.249224
## iter 220 value 357.084233
## iter 230 value 354.454981
## iter 240 value 350.453309
## iter 250 value 343.570359
## iter 260 value 339.020920
## iter 270 value 336.088528
## iter 280 value 331.644765
## iter 290 value 322.467801
## iter 300 value 315.818035
## iter 310 value 314.054453
## iter 320 value 312.416131
## iter 330 value 310.474970
## iter 340 value 308.439010
## iter 350 value 305.708077
## iter 360 value 304.027757
## iter 370 value 303.332448
## iter 380 value 302.833453
## iter 390 value 302.089683
## iter 400 value 301.738236
## iter 410 value 301.537231
## iter 420 value 301.386185
## iter 430 value 301.330088
## iter 440 value 301.327301
## iter 450 value 301.320954
## iter 460 value 301.317323
## iter 470 value 301.314342
## iter 480 value 301.308576
## iter 490 value 301.303215
## iter 500 value 301.292486
## final  value 301.292486 
## stopped after 500 iterations
## # weights:  141
## initial  value 1454602.489563 
## iter  10 value 1371.006540
## iter  20 value 1064.883584
## iter  30 value 920.586935
## iter  40 value 832.871982
## iter  50 value 755.224488
## iter  60 value 722.918134
## iter  70 value 691.722827
## iter  80 value 625.468541
## iter  90 value 531.719855
## iter 100 value 459.228664
## iter 110 value 416.374202
## iter 120 value 384.926575
## iter 130 value 362.053091
## iter 140 value 338.350870
## iter 150 value 322.881428
## iter 160 value 305.181006
## iter 170 value 292.996463
## iter 180 value 278.766429
## iter 190 value 268.789712
## iter 200 value 259.843335
## iter 210 value 253.213056
## iter 220 value 248.059874
## iter 230 value 244.239876
## iter 240 value 239.401114
## iter 250 value 235.319164
## iter 260 value 230.412358
## iter 270 value 226.651947
## iter 280 value 223.527392
## iter 290 value 221.817018
## iter 300 value 219.647433
## iter 310 value 215.007847
## iter 320 value 212.237471
## iter 330 value 208.782022
## iter 340 value 205.814818
## iter 350 value 203.362370
## iter 360 value 200.381043
## iter 370 value 197.569194
## iter 380 value 192.738914
## iter 390 value 189.310304
## iter 400 value 187.015938
## iter 410 value 185.022402
## iter 420 value 182.075660
## iter 430 value 178.169691
## iter 440 value 176.305641
## iter 450 value 174.420466
## iter 460 value 172.636490
## iter 470 value 171.318235
## iter 480 value 170.494596
## iter 490 value 169.570920
## iter 500 value 169.021540
## final  value 169.021540 
## stopped after 500 iterations
## # weights:  15
## initial  value 1392144.189633 
## iter  10 value 6433.812340
## iter  20 value 2676.120080
## iter  30 value 1811.927804
## iter  40 value 1682.292750
## iter  50 value 1476.332313
## iter  60 value 1315.111942
## iter  70 value 1274.292169
## iter  80 value 1224.564562
## iter  90 value 1160.704046
## iter 100 value 1146.391149
## iter 110 value 1143.077780
## iter 120 value 1138.916403
## iter 130 value 1137.219353
## iter 140 value 1136.078993
## iter 150 value 1133.626263
## iter 160 value 1132.813788
## iter 170 value 1132.331805
## iter 180 value 1132.009354
## iter 190 value 1131.632142
## iter 200 value 1131.453202
## iter 210 value 1131.224373
## final  value 1130.792211 
## converged
## # weights:  36
## initial  value 1359729.007212 
## iter  10 value 17691.428547
## iter  20 value 3955.154273
## iter  30 value 2937.702788
## iter  40 value 2024.870173
## iter  50 value 1712.111828
## iter  60 value 1512.661991
## iter  70 value 1393.587728
## iter  80 value 1307.075522
## iter  90 value 1261.002195
## iter 100 value 1154.092973
## iter 110 value 1044.166093
## iter 120 value 1003.069491
## iter 130 value 992.442906
## iter 140 value 980.014704
## iter 150 value 968.787261
## iter 160 value 966.132179
## iter 170 value 962.360079
## iter 180 value 958.594611
## iter 190 value 954.023293
## iter 200 value 945.278413
## iter 210 value 943.328843
## iter 220 value 941.057242
## iter 230 value 940.661520
## iter 240 value 940.444540
## iter 250 value 938.988496
## iter 260 value 936.402156
## iter 270 value 934.280806
## iter 280 value 933.513869
## iter 290 value 932.854822
## iter 300 value 932.590950
## iter 310 value 932.462597
## iter 320 value 932.319529
## iter 330 value 931.955702
## iter 340 value 930.315471
## iter 350 value 929.575739
## iter 360 value 928.710537
## iter 370 value 928.018720
## iter 380 value 928.004621
## iter 390 value 927.992075
## iter 400 value 927.969310
## iter 410 value 927.289716
## iter 420 value 926.803561
## iter 430 value 926.156505
## iter 440 value 924.715305
## iter 450 value 924.337551
## iter 460 value 923.774407
## iter 470 value 920.706096
## iter 480 value 919.435904
## iter 490 value 917.840575
## iter 500 value 915.410804
## final  value 915.410804 
## stopped after 500 iterations
## # weights:  71
## initial  value 1452113.879510 
## iter  10 value 16491.729511
## iter  20 value 12327.550742
## iter  30 value 8290.850180
## iter  40 value 5453.143542
## iter  50 value 3034.536798
## iter  60 value 1477.509172
## iter  70 value 1181.044809
## iter  80 value 1108.703281
## iter  90 value 1071.841831
## iter 100 value 1058.282704
## iter 110 value 1038.735706
## iter 120 value 1027.336030
## iter 130 value 1014.316595
## iter 140 value 1005.312422
## iter 150 value 999.091054
## iter 160 value 994.553662
## iter 170 value 990.532483
## iter 180 value 988.559533
## iter 190 value 984.556771
## iter 200 value 977.588167
## iter 210 value 966.704296
## iter 220 value 957.247276
## iter 230 value 949.341368
## iter 240 value 947.068849
## iter 250 value 946.101001
## iter 260 value 942.948448
## iter 270 value 940.805811
## iter 280 value 940.424509
## iter 290 value 940.227769
## iter 300 value 939.835766
## iter 310 value 938.922172
## iter 320 value 938.684092
## iter 330 value 938.289945
## iter 340 value 937.496729
## iter 350 value 936.541986
## iter 360 value 935.175009
## iter 370 value 933.424881
## iter 380 value 933.157244
## iter 390 value 933.056751
## iter 400 value 932.682537
## iter 410 value 932.488279
## iter 420 value 932.179834
## iter 430 value 931.870476
## iter 440 value 931.653296
## iter 450 value 931.492243
## iter 460 value 931.378489
## iter 470 value 931.004088
## iter 480 value 930.747140
## iter 490 value 930.641320
## iter 500 value 930.595132
## final  value 930.595132 
## stopped after 500 iterations
## # weights:  106
## initial  value 1363546.514924 
## iter  10 value 1303.333578
## iter  20 value 1003.741511
## iter  30 value 898.282158
## iter  40 value 829.601040
## iter  50 value 773.891295
## iter  60 value 726.391702
## iter  70 value 676.355824
## iter  80 value 640.657594
## iter  90 value 618.890422
## iter 100 value 577.933925
## iter 110 value 539.051787
## iter 120 value 508.940644
## iter 130 value 492.026084
## iter 140 value 478.999592
## iter 150 value 462.477896
## iter 160 value 445.044131
## iter 170 value 427.290209
## iter 180 value 417.963469
## iter 190 value 411.927311
## iter 200 value 405.169824
## iter 210 value 394.693626
## iter 220 value 388.628775
## iter 230 value 385.675041
## iter 240 value 378.918758
## iter 250 value 368.577004
## iter 260 value 362.168470
## iter 270 value 358.706260
## iter 280 value 352.601800
## iter 290 value 348.672214
## iter 300 value 344.775145
## iter 310 value 339.740245
## iter 320 value 337.274193
## iter 330 value 332.083732
## iter 340 value 324.286479
## iter 350 value 320.604535
## iter 360 value 318.145984
## iter 370 value 316.806642
## iter 380 value 315.764522
## iter 390 value 314.830735
## iter 400 value 313.947746
## iter 410 value 313.437142
## iter 420 value 313.004588
## iter 430 value 312.643572
## iter 440 value 312.608142
## iter 450 value 312.535936
## iter 460 value 312.489478
## iter 470 value 312.453986
## iter 480 value 312.331695
## iter 490 value 312.230971
## iter 500 value 312.205064
## final  value 312.205064 
## stopped after 500 iterations
## # weights:  141
## initial  value 1378619.868739 
## iter  10 value 1858.928226
## iter  20 value 1062.907500
## iter  30 value 935.561001
## iter  40 value 870.301321
## iter  50 value 759.180047
## iter  60 value 693.564592
## iter  70 value 637.995490
## iter  80 value 586.961667
## iter  90 value 541.337192
## iter 100 value 501.698879
## iter 110 value 468.100924
## iter 120 value 450.795621
## iter 130 value 433.122732
## iter 140 value 415.159483
## iter 150 value 395.681592
## iter 160 value 383.212258
## iter 170 value 367.002818
## iter 180 value 347.899697
## iter 190 value 334.994893
## iter 200 value 324.765103
## iter 210 value 316.048628
## iter 220 value 306.366314
## iter 230 value 300.964829
## iter 240 value 297.578954
## iter 250 value 293.490389
## iter 260 value 288.899638
## iter 270 value 285.207792
## iter 280 value 281.116034
## iter 290 value 279.382592
## iter 300 value 277.948865
## iter 310 value 276.208802
## iter 320 value 273.220517
## iter 330 value 270.325901
## iter 340 value 265.224281
## iter 350 value 259.221778
## iter 360 value 251.302235
## iter 370 value 243.879029
## iter 380 value 237.093860
## iter 390 value 229.293639
## iter 400 value 217.809341
## iter 410 value 209.193311
## iter 420 value 204.518701
## iter 430 value 200.814327
## iter 440 value 196.174469
## iter 450 value 193.063760
## iter 460 value 190.469498
## iter 470 value 186.892697
## iter 480 value 184.033892
## iter 490 value 182.229653
## iter 500 value 180.302852
## final  value 180.302852 
## stopped after 500 iterations
## # weights:  15
## initial  value 1394897.212124 
## iter  10 value 5162.713161
## iter  20 value 5078.609650
## iter  30 value 4801.203746
## iter  40 value 3074.250956
## iter  50 value 2154.882206
## iter  60 value 1924.421827
## iter  70 value 1870.734967
## iter  80 value 1834.852688
## iter  90 value 1823.743128
## iter 100 value 1818.571540
## iter 110 value 1818.536299
## final  value 1818.536099 
## converged
## # weights:  36
## initial  value 1401282.851012 
## iter  10 value 299377.549970
## iter  20 value 46719.334411
## iter  30 value 18825.354273
## iter  40 value 3922.768110
## iter  50 value 2157.894096
## iter  60 value 1731.840851
## iter  70 value 1456.286086
## iter  80 value 1338.321858
## iter  90 value 1232.041351
## iter 100 value 1130.596183
## iter 110 value 1019.648902
## iter 120 value 993.516137
## iter 130 value 974.765751
## iter 140 value 953.094140
## iter 150 value 949.117352
## iter 160 value 947.951398
## iter 170 value 942.732152
## iter 180 value 937.182200
## iter 190 value 929.935079
## iter 200 value 925.579572
## iter 210 value 922.699401
## iter 220 value 919.457786
## iter 230 value 919.134128
## iter 240 value 918.217481
## iter 250 value 917.894544
## iter 260 value 917.596083
## iter 270 value 916.856352
## iter 280 value 915.798906
## iter 290 value 914.423154
## iter 300 value 914.096331
## iter 310 value 914.003027
## iter 320 value 913.857292
## iter 330 value 913.802022
## iter 340 value 913.778654
## iter 350 value 913.450678
## iter 360 value 912.620186
## iter 370 value 912.186705
## iter 380 value 912.170843
## iter 390 value 912.001256
## iter 400 value 911.961998
## iter 410 value 911.936799
## iter 420 value 911.924235
## iter 430 value 911.888451
## iter 440 value 911.804263
## iter 450 value 911.797198
## iter 460 value 911.757936
## iter 470 value 911.749491
## iter 480 value 911.745796
## iter 490 value 911.735760
## iter 500 value 911.571557
## final  value 911.571557 
## stopped after 500 iterations
## # weights:  71
## initial  value 1376017.317020 
## iter  10 value 3464.612535
## iter  20 value 1751.165603
## iter  30 value 1422.929005
## iter  40 value 1117.303075
## iter  50 value 955.655064
## iter  60 value 888.847305
## iter  70 value 862.120361
## iter  80 value 839.958883
## iter  90 value 827.557725
## iter 100 value 820.683860
## iter 110 value 811.722289
## iter 120 value 804.313757
## iter 130 value 795.707076
## iter 140 value 785.468441
## iter 150 value 781.903630
## iter 160 value 779.800490
## iter 170 value 776.814668
## iter 180 value 770.670435
## iter 190 value 766.385559
## iter 200 value 762.837572
## iter 210 value 749.070654
## iter 220 value 744.160498
## iter 230 value 739.437877
## iter 240 value 736.551619
## iter 250 value 734.489056
## iter 260 value 733.560971
## iter 270 value 733.392458
## iter 280 value 733.145980
## iter 290 value 732.611996
## iter 300 value 732.570223
## iter 310 value 732.457219
## iter 320 value 732.338296
## iter 330 value 732.220644
## iter 340 value 731.745401
## iter 350 value 731.521834
## iter 360 value 731.517942
## iter 370 value 731.506487
## iter 380 value 731.504802
## iter 390 value 731.495499
## iter 400 value 731.461477
## iter 410 value 731.441174
## iter 420 value 731.427676
## iter 430 value 731.421643
## iter 440 value 731.421121
## iter 440 value 731.421116
## iter 440 value 731.421115
## final  value 731.421115 
## converged
## # weights:  106
## initial  value 1407377.914299 
## iter  10 value 2074.402667
## iter  20 value 1201.432719
## iter  30 value 1042.422082
## iter  40 value 923.938316
## iter  50 value 831.032753
## iter  60 value 710.205955
## iter  70 value 651.342813
## iter  80 value 615.889866
## iter  90 value 583.348638
## iter 100 value 529.034721
## iter 110 value 504.754408
## iter 120 value 487.981531
## iter 130 value 470.585053
## iter 140 value 460.747388
## iter 150 value 452.717126
## iter 160 value 443.235224
## iter 170 value 435.857171
## iter 180 value 421.020237
## iter 190 value 401.809648
## iter 200 value 393.972519
## iter 210 value 387.049904
## iter 220 value 383.657858
## iter 230 value 381.139890
## iter 240 value 378.160243
## iter 250 value 376.014390
## iter 260 value 372.217909
## iter 270 value 368.573342
## iter 280 value 365.808981
## iter 290 value 355.625908
## iter 300 value 351.126859
## iter 310 value 347.943010
## iter 320 value 343.015882
## iter 330 value 340.542429
## iter 340 value 337.334651
## iter 350 value 330.831954
## iter 360 value 325.399510
## iter 370 value 321.731734
## iter 380 value 319.617890
## iter 390 value 318.451430
## iter 400 value 316.694868
## iter 410 value 315.176868
## iter 420 value 314.780802
## iter 430 value 314.510744
## iter 440 value 314.495566
## iter 450 value 314.479296
## iter 460 value 314.444859
## iter 470 value 314.424583
## iter 480 value 314.406412
## iter 490 value 314.394975
## iter 500 value 314.069708
## final  value 314.069708 
## stopped after 500 iterations
## # weights:  141
## initial  value 1388056.072870 
## iter  10 value 1684.295365
## iter  20 value 1153.818933
## iter  30 value 982.515877
## iter  40 value 884.983327
## iter  50 value 791.116418
## iter  60 value 705.268872
## iter  70 value 649.254072
## iter  80 value 579.855527
## iter  90 value 527.283304
## iter 100 value 480.903862
## iter 110 value 446.935913
## iter 120 value 424.665189
## iter 130 value 399.135852
## iter 140 value 370.709539
## iter 150 value 357.444193
## iter 160 value 347.673558
## iter 170 value 335.178396
## iter 180 value 321.796647
## iter 190 value 311.954455
## iter 200 value 300.054588
## iter 210 value 288.263785
## iter 220 value 273.995826
## iter 230 value 266.532930
## iter 240 value 261.576882
## iter 250 value 255.931468
## iter 260 value 247.748924
## iter 270 value 240.945897
## iter 280 value 236.453058
## iter 290 value 234.334535
## iter 300 value 233.439519
## iter 310 value 231.159725
## iter 320 value 228.314014
## iter 330 value 224.902002
## iter 340 value 218.455239
## iter 350 value 207.358814
## iter 360 value 200.892706
## iter 370 value 197.616852
## iter 380 value 194.147778
## iter 390 value 190.508808
## iter 400 value 187.722129
## iter 410 value 183.688059
## iter 420 value 179.321355
## iter 430 value 173.733639
## iter 440 value 168.635558
## iter 450 value 163.646793
## iter 460 value 161.347154
## iter 470 value 159.199635
## iter 480 value 157.566174
## iter 490 value 154.744408
## iter 500 value 153.144467
## final  value 153.144467 
## stopped after 500 iterations
## # weights:  15
## initial  value 1406384.173774 
## iter  10 value 13770.535132
## iter  20 value 6442.126009
## iter  30 value 3961.337201
## iter  40 value 3530.230923
## iter  50 value 2129.021526
## iter  60 value 1642.019028
## iter  70 value 1609.446071
## iter  80 value 1560.165248
## iter  90 value 1527.023094
## iter 100 value 1524.871504
## iter 110 value 1522.102226
## iter 120 value 1521.694254
## final  value 1521.694064 
## converged
## # weights:  36
## initial  value 1419345.938229 
## iter  10 value 10285.485097
## iter  20 value 2133.955217
## iter  30 value 1585.293843
## iter  40 value 1290.235646
## iter  50 value 1233.916035
## iter  60 value 1208.393003
## iter  70 value 1195.516965
## iter  80 value 1188.829209
## iter  90 value 1185.736841
## iter 100 value 1176.333956
## iter 110 value 1170.663831
## iter 120 value 1161.185878
## iter 130 value 1150.371017
## iter 140 value 1143.791189
## iter 150 value 1141.248770
## iter 160 value 1140.060515
## iter 170 value 1137.697619
## iter 180 value 1136.325388
## iter 190 value 1134.972170
## iter 200 value 1129.388113
## iter 210 value 1127.405256
## iter 220 value 1127.364939
## iter 220 value 1127.364928
## final  value 1127.364928 
## converged
## # weights:  71
## initial  value 1418208.299342 
## iter  10 value 1511.385059
## iter  20 value 1201.424527
## iter  30 value 1106.939757
## iter  40 value 1034.609531
## iter  50 value 948.160662
## iter  60 value 890.552004
## iter  70 value 867.892470
## iter  80 value 849.904880
## iter  90 value 832.198211
## iter 100 value 816.405006
## iter 110 value 805.896390
## iter 120 value 797.676841
## iter 130 value 788.006387
## iter 140 value 783.980251
## iter 150 value 780.190004
## iter 160 value 777.333317
## iter 170 value 772.997289
## iter 180 value 769.450843
## iter 190 value 767.194185
## iter 200 value 765.620129
## iter 210 value 764.966629
## iter 220 value 764.893326
## iter 230 value 764.816231
## iter 240 value 763.216966
## iter 250 value 759.392214
## iter 260 value 755.597226
## iter 270 value 752.209103
## iter 280 value 750.418603
## iter 290 value 749.957810
## iter 300 value 749.910971
## iter 310 value 749.792740
## iter 320 value 749.710589
## iter 330 value 749.066642
## iter 340 value 745.329790
## iter 350 value 739.117790
## iter 360 value 738.097433
## iter 370 value 738.085150
## final  value 738.085114 
## converged
## # weights:  106
## initial  value 1449985.119957 
## iter  10 value 1758.890706
## iter  20 value 1214.730413
## iter  30 value 1045.211963
## iter  40 value 932.133679
## iter  50 value 886.510521
## iter  60 value 847.062979
## iter  70 value 810.493104
## iter  80 value 784.385695
## iter  90 value 765.576851
## iter 100 value 752.741255
## iter 110 value 738.455595
## iter 120 value 730.039813
## iter 130 value 716.952158
## iter 140 value 706.587225
## iter 150 value 697.176892
## iter 160 value 691.003893
## iter 170 value 686.186771
## iter 180 value 682.886178
## iter 190 value 678.854083
## iter 200 value 676.074763
## iter 210 value 673.910854
## iter 220 value 672.925219
## iter 230 value 670.982874
## iter 240 value 667.475003
## iter 250 value 665.475129
## iter 260 value 663.798610
## iter 270 value 662.945312
## iter 280 value 662.202662
## iter 290 value 659.282755
## iter 300 value 656.427126
## iter 310 value 646.784586
## iter 320 value 634.594268
## iter 330 value 626.846997
## iter 340 value 619.842910
## iter 350 value 617.088115
## iter 360 value 616.137738
## iter 370 value 615.775151
## iter 380 value 615.587091
## iter 390 value 615.289510
## iter 400 value 615.200831
## iter 410 value 615.177809
## iter 420 value 615.175197
## final  value 615.174503 
## converged
## # weights:  141
## initial  value 1442019.296828 
## iter  10 value 2135.762394
## iter  20 value 1245.768955
## iter  30 value 1055.972566
## iter  40 value 907.837633
## iter  50 value 813.404619
## iter  60 value 758.620533
## iter  70 value 718.241072
## iter  80 value 681.105110
## iter  90 value 663.018197
## iter 100 value 648.306951
## iter 110 value 636.832177
## iter 120 value 627.259535
## iter 130 value 619.276473
## iter 140 value 611.373841
## iter 150 value 605.894464
## iter 160 value 597.688702
## iter 170 value 593.212221
## iter 180 value 587.419283
## iter 190 value 583.198573
## iter 200 value 576.744091
## iter 210 value 570.306860
## iter 220 value 563.239511
## iter 230 value 550.595925
## iter 240 value 544.382092
## iter 250 value 537.447854
## iter 260 value 533.626267
## iter 270 value 527.581366
## iter 280 value 524.948526
## iter 290 value 523.439255
## iter 300 value 521.171975
## iter 310 value 517.264828
## iter 320 value 512.162345
## iter 330 value 505.485240
## iter 340 value 495.151632
## iter 350 value 488.985637
## iter 360 value 485.890829
## iter 370 value 484.029069
## iter 380 value 482.026200
## iter 390 value 478.993648
## iter 400 value 476.991262
## iter 410 value 475.273422
## iter 420 value 472.124650
## iter 430 value 469.299592
## iter 440 value 466.348386
## iter 450 value 464.065269
## iter 460 value 462.960269
## iter 470 value 461.886329
## iter 480 value 461.574195
## iter 490 value 461.484284
## iter 500 value 461.456913
## final  value 461.456913 
## stopped after 500 iterations
## # weights:  15
## initial  value 1406748.916990 
## iter  10 value 5344.778159
## iter  20 value 4171.474859
## iter  30 value 3762.020870
## iter  40 value 3591.519595
## iter  50 value 3169.823943
## iter  60 value 2755.674271
## iter  70 value 1931.314589
## iter  80 value 1591.791014
## iter  90 value 1409.387507
## iter 100 value 1331.425372
## iter 110 value 1270.718900
## iter 120 value 1260.124728
## iter 130 value 1258.479596
## iter 140 value 1252.912787
## iter 150 value 1249.355674
## iter 160 value 1249.161318
## iter 170 value 1248.235828
## iter 180 value 1247.843143
## iter 190 value 1247.828647
## iter 200 value 1247.804769
## final  value 1247.804612 
## converged
## # weights:  36
## initial  value 1404654.402350 
## iter  10 value 5467.228743
## iter  20 value 3625.834662
## iter  30 value 3079.268039
## iter  40 value 2932.355537
## iter  50 value 2418.294763
## iter  60 value 1667.722069
## iter  70 value 1509.250946
## iter  80 value 1332.558187
## iter  90 value 1286.847206
## iter 100 value 1269.895138
## iter 110 value 1251.510245
## iter 120 value 1244.060223
## iter 130 value 1218.629738
## iter 140 value 1196.282727
## iter 150 value 1149.970287
## iter 160 value 1099.627041
## iter 170 value 1066.795443
## iter 180 value 1016.979808
## iter 190 value 995.532014
## iter 200 value 989.616527
## iter 210 value 988.702919
## iter 220 value 983.951636
## iter 230 value 970.995146
## iter 240 value 963.502618
## iter 250 value 961.800353
## iter 260 value 961.319842
## iter 270 value 961.045437
## iter 280 value 961.036412
## iter 290 value 960.997069
## iter 300 value 960.892077
## iter 310 value 960.795150
## iter 320 value 960.756013
## iter 330 value 960.722899
## iter 340 value 960.566816
## iter 350 value 960.508370
## iter 360 value 960.501237
## iter 370 value 960.387318
## iter 380 value 959.168468
## iter 390 value 936.004814
## iter 400 value 929.486939
## iter 410 value 928.064978
## iter 420 value 926.698322
## iter 430 value 926.637411
## iter 440 value 926.497954
## iter 450 value 925.969762
## iter 460 value 925.702800
## iter 470 value 925.630139
## iter 480 value 925.621276
## final  value 925.620160 
## converged
## # weights:  71
## initial  value 1448594.288173 
## iter  10 value 1998.884294
## iter  20 value 1249.210458
## iter  30 value 1039.331207
## iter  40 value 949.907337
## iter  50 value 885.072117
## iter  60 value 836.653176
## iter  70 value 803.250524
## iter  80 value 772.197971
## iter  90 value 715.745609
## iter 100 value 682.632037
## iter 110 value 669.647726
## iter 120 value 658.561556
## iter 130 value 647.574728
## iter 140 value 633.092437
## iter 150 value 629.414717
## iter 160 value 628.385676
## iter 170 value 626.133596
## iter 180 value 619.577671
## iter 190 value 614.019318
## iter 200 value 606.773262
## iter 210 value 591.076212
## iter 220 value 579.598162
## iter 230 value 573.723423
## iter 240 value 565.237458
## iter 250 value 562.430163
## iter 260 value 560.967748
## iter 270 value 560.312162
## iter 280 value 559.792420
## iter 290 value 559.499271
## iter 300 value 559.482463
## iter 310 value 559.434718
## iter 320 value 559.343581
## iter 330 value 559.220237
## iter 340 value 559.174525
## iter 350 value 559.080966
## iter 360 value 559.064228
## iter 370 value 559.060605
## final  value 559.060347 
## converged
## # weights:  106
## initial  value 1371057.175926 
## iter  10 value 1603.382123
## iter  20 value 1100.374844
## iter  30 value 925.027665
## iter  40 value 745.631744
## iter  50 value 655.625838
## iter  60 value 589.808411
## iter  70 value 556.493895
## iter  80 value 537.184875
## iter  90 value 522.169353
## iter 100 value 509.229286
## iter 110 value 499.542885
## iter 120 value 492.351116
## iter 130 value 481.882074
## iter 140 value 466.555841
## iter 150 value 452.173222
## iter 160 value 438.563390
## iter 170 value 429.011152
## iter 180 value 423.009685
## iter 190 value 411.752831
## iter 200 value 400.183627
## iter 210 value 393.507508
## iter 220 value 390.993711
## iter 230 value 388.846996
## iter 240 value 384.655022
## iter 250 value 380.523247
## iter 260 value 373.929378
## iter 270 value 366.448851
## iter 280 value 361.663323
## iter 290 value 357.166943
## iter 300 value 350.395028
## iter 310 value 344.855082
## iter 320 value 338.122554
## iter 330 value 331.705616
## iter 340 value 325.879371
## iter 350 value 320.352971
## iter 360 value 316.539782
## iter 370 value 313.477916
## iter 380 value 311.284552
## iter 390 value 309.131812
## iter 400 value 305.835875
## iter 410 value 302.043903
## iter 420 value 298.777507
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## iter 460 value 295.993659
## iter 470 value 295.440906
## iter 480 value 294.851147
## iter 490 value 294.474237
## iter 500 value 294.139842
## final  value 294.139842 
## stopped after 500 iterations
## # weights:  141
## initial  value 1433748.111499 
## iter  10 value 1716.685914
## iter  20 value 1126.974194
## iter  30 value 848.670411
## iter  40 value 731.991249
## iter  50 value 647.619439
## iter  60 value 554.629487
## iter  70 value 514.718376
## iter  80 value 473.565282
## iter  90 value 445.613081
## iter 100 value 420.690106
## iter 110 value 393.139626
## iter 120 value 373.615705
## iter 130 value 346.903443
## iter 140 value 323.140093
## iter 150 value 300.935461
## iter 160 value 279.244054
## iter 170 value 270.928587
## iter 180 value 261.954418
## iter 190 value 254.321715
## iter 200 value 246.904057
## iter 210 value 236.881883
## iter 220 value 230.048595
## iter 230 value 226.737123
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## iter 250 value 221.362550
## iter 260 value 216.849894
## iter 270 value 214.548149
## iter 280 value 212.845532
## iter 290 value 212.131582
## iter 300 value 211.864822
## iter 310 value 211.321963
## iter 320 value 210.552533
## iter 330 value 209.330324
## iter 340 value 207.089743
## iter 350 value 202.623631
## iter 360 value 200.154507
## iter 370 value 197.557763
## iter 380 value 194.735735
## iter 390 value 193.101472
## iter 400 value 191.900433
## iter 410 value 189.408658
## iter 420 value 185.753655
## iter 430 value 182.121570
## iter 440 value 179.993106
## iter 450 value 178.817384
## iter 460 value 177.556732
## iter 470 value 176.637584
## iter 480 value 176.057998
## iter 490 value 175.813406
## iter 500 value 175.678030
## final  value 175.678030 
## stopped after 500 iterations
## # weights:  15
## initial  value 1414275.057729 
## iter  10 value 15993.717911
## iter  20 value 14452.202363
## iter  30 value 7909.059426
## iter  40 value 4864.348001
## iter  50 value 2074.802229
## iter  60 value 1869.589327
## iter  70 value 1854.122154
## iter  80 value 1773.932000
## iter  90 value 1693.198275
## iter 100 value 1652.898882
## iter 110 value 1646.097513
## iter 120 value 1630.981011
## iter 130 value 1624.764947
## iter 140 value 1619.108699
## iter 150 value 1618.644734
## iter 160 value 1614.566990
## iter 170 value 1612.545623
## iter 180 value 1612.505240
## iter 190 value 1608.285391
## iter 200 value 1598.591962
## iter 210 value 1504.491139
## iter 220 value 1473.929736
## iter 230 value 1466.841804
## iter 240 value 1457.972653
## iter 250 value 1456.437960
## iter 260 value 1456.286939
## iter 270 value 1455.189793
## iter 280 value 1455.049900
## iter 290 value 1455.028425
## iter 300 value 1454.814431
## iter 310 value 1454.605030
## iter 320 value 1454.583014
## iter 330 value 1454.282042
## iter 340 value 1454.258043
## iter 350 value 1454.252589
## iter 360 value 1454.237810
## iter 370 value 1454.202741
## final  value 1454.195031 
## converged
## # weights:  36
## initial  value 1402841.127600 
## iter  10 value 228827.546118
## iter  20 value 10211.092963
## iter  30 value 6251.151183
## iter  40 value 5867.695134
## iter  50 value 5609.435087
## iter  60 value 5548.416626
## iter  70 value 5546.900389
## iter  80 value 5438.673466
## iter  90 value 5074.979683
## iter 100 value 4484.504870
## iter 110 value 4073.955045
## iter 120 value 3408.894762
## iter 130 value 2776.611584
## iter 140 value 2257.032629
## iter 150 value 1869.538054
## iter 160 value 1777.757859
## iter 170 value 1715.453915
## iter 180 value 1664.946109
## iter 190 value 1657.823633
## iter 200 value 1565.906107
## iter 210 value 1523.844259
## iter 220 value 1504.158555
## iter 230 value 1500.596163
## iter 240 value 1494.814806
## iter 250 value 1483.980230
## iter 260 value 1479.722417
## iter 270 value 1470.198098
## iter 280 value 1468.757975
## iter 290 value 1463.498356
## iter 300 value 1461.558397
## iter 310 value 1460.552166
## iter 320 value 1460.280731
## iter 330 value 1459.225552
## iter 340 value 1458.314760
## iter 350 value 1457.799016
## iter 360 value 1437.935802
## iter 370 value 1422.248602
## iter 380 value 1403.731008
## iter 390 value 1382.191780
## iter 400 value 1381.367892
## iter 410 value 1379.709743
## iter 420 value 1376.487023
## iter 430 value 1354.741249
## iter 440 value 1345.648898
## iter 450 value 1345.430448
## iter 460 value 1345.325696
## iter 470 value 1344.149590
## iter 480 value 1343.522160
## iter 490 value 1342.892640
## iter 500 value 1341.942434
## final  value 1341.942434 
## stopped after 500 iterations
## # weights:  71
## initial  value 1449975.565530 
## iter  10 value 40444.399758
## iter  20 value 6075.795049
## iter  30 value 3862.815550
## iter  40 value 3083.978612
## iter  50 value 2662.046461
## iter  60 value 2322.254135
## iter  70 value 1954.854662
## iter  80 value 1583.901698
## iter  90 value 1444.266789
## iter 100 value 1398.010418
## iter 110 value 1372.933580
## iter 120 value 1351.217501
## iter 130 value 1317.944425
## iter 140 value 1276.713502
## iter 150 value 1269.099239
## iter 160 value 1242.391358
## iter 170 value 1157.246799
## iter 180 value 1057.847438
## iter 190 value 1031.591334
## iter 200 value 996.808407
## iter 210 value 967.564529
## iter 220 value 939.575163
## iter 230 value 928.066501
## iter 240 value 918.945303
## iter 250 value 888.570445
## iter 260 value 882.932774
## iter 270 value 880.446083
## iter 280 value 879.776602
## iter 290 value 879.083103
## iter 300 value 878.612049
## iter 310 value 876.233187
## iter 320 value 874.387299
## iter 330 value 871.969478
## iter 340 value 868.290497
## iter 350 value 865.042164
## iter 360 value 864.015139
## iter 370 value 863.599565
## iter 380 value 863.371594
## iter 390 value 862.988803
## iter 400 value 862.974959
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## iter 420 value 860.171656
## iter 430 value 847.275772
## iter 440 value 838.539382
## iter 450 value 832.408776
## iter 460 value 827.071409
## iter 470 value 823.829291
## iter 480 value 822.432562
## iter 490 value 821.975579
## iter 500 value 821.735226
## final  value 821.735226 
## stopped after 500 iterations
## # weights:  106
## initial  value 1336980.291867 
## iter  10 value 1382.243435
## iter  20 value 1128.860289
## iter  30 value 1021.675856
## iter  40 value 914.316224
## iter  50 value 837.577559
## iter  60 value 763.040455
## iter  70 value 683.074378
## iter  80 value 621.558741
## iter  90 value 592.998099
## iter 100 value 533.299534
## iter 110 value 505.181607
## iter 120 value 492.730733
## iter 130 value 477.285793
## iter 140 value 456.242617
## iter 150 value 439.100116
## iter 160 value 427.142554
## iter 170 value 416.589542
## iter 180 value 408.683610
## iter 190 value 397.428769
## iter 200 value 387.591494
## iter 210 value 380.368936
## iter 220 value 378.674893
## iter 230 value 377.382124
## iter 240 value 374.804496
## iter 250 value 371.315285
## iter 260 value 369.644912
## iter 270 value 368.158298
## iter 280 value 365.870123
## iter 290 value 364.169050
## iter 300 value 359.930813
## iter 310 value 352.908464
## iter 320 value 350.849746
## iter 330 value 349.928311
## iter 340 value 349.187324
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## iter 360 value 348.258115
## iter 370 value 346.939034
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## iter 390 value 346.110817
## iter 400 value 346.044970
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## iter 440 value 345.756103
## iter 450 value 345.754809
## iter 460 value 345.753930
## iter 470 value 345.752414
## iter 480 value 345.748892
## iter 490 value 345.740443
## iter 500 value 345.737004
## final  value 345.737004 
## stopped after 500 iterations
## # weights:  141
## initial  value 1413241.499437 
## iter  10 value 2279.795097
## iter  20 value 1175.898175
## iter  30 value 976.188393
## iter  40 value 811.499283
## iter  50 value 695.977927
## iter  60 value 640.718240
## iter  70 value 585.698489
## iter  80 value 533.511471
## iter  90 value 490.506825
## iter 100 value 458.070088
## iter 110 value 429.159668
## iter 120 value 386.702555
## iter 130 value 351.365682
## iter 140 value 321.730384
## iter 150 value 302.347509
## iter 160 value 290.376928
## iter 170 value 283.441782
## iter 180 value 274.979286
## iter 190 value 266.262022
## iter 200 value 256.945514
## iter 210 value 249.132529
## iter 220 value 242.318429
## iter 230 value 235.534123
## iter 240 value 230.080300
## iter 250 value 226.376682
## iter 260 value 221.633291
## iter 270 value 217.344036
## iter 280 value 214.873475
## iter 290 value 213.347894
## iter 300 value 212.851527
## iter 310 value 212.199137
## iter 320 value 211.330556
## iter 330 value 210.471837
## iter 340 value 208.975116
## iter 350 value 206.828116
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## iter 370 value 200.508704
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## iter 390 value 194.368821
## iter 400 value 191.736908
## iter 410 value 190.092639
## iter 420 value 188.672407
## iter 430 value 185.550395
## iter 440 value 182.806708
## iter 450 value 181.023997
## iter 460 value 180.226224
## iter 470 value 179.673985
## iter 480 value 178.550208
## iter 490 value 177.661683
## iter 500 value 177.302370
## final  value 177.302370 
## stopped after 500 iterations
## # weights:  15
## initial  value 1398267.615428 
## iter  10 value 11619.407758
## iter  20 value 5352.990587
## iter  30 value 4647.506039
## iter  40 value 4461.688077
## iter  50 value 4432.359454
## iter  60 value 4408.846050
## iter  70 value 3999.030467
## iter  80 value 3908.154365
## iter  90 value 3897.943932
## iter 100 value 3892.703329
## iter 110 value 3892.294242
## iter 120 value 3886.270935
## iter 130 value 3870.236258
## iter 140 value 3864.745158
## iter 150 value 3818.714913
## iter 160 value 3758.256686
## iter 170 value 3734.727130
## iter 180 value 3711.406103
## iter 190 value 3215.751492
## iter 200 value 2259.044848
## iter 210 value 1709.045601
## iter 220 value 1645.635927
## iter 230 value 1592.875361
## iter 240 value 1564.872231
## iter 250 value 1551.023979
## iter 260 value 1547.415262
## iter 270 value 1539.517998
## iter 280 value 1537.306871
## iter 290 value 1536.106282
## iter 300 value 1533.038290
## iter 310 value 1531.696399
## iter 320 value 1531.654871
## iter 330 value 1529.604686
## iter 340 value 1528.612561
## iter 350 value 1528.599485
## iter 360 value 1528.008299
## iter 370 value 1527.704097
## final  value 1527.704063 
## converged
## # weights:  36
## initial  value 1425884.984400 
## iter  10 value 4274.641900
## iter  20 value 3328.818414
## iter  30 value 2467.323987
## iter  40 value 1882.492559
## iter  50 value 1753.893489
## iter  60 value 1724.666896
## iter  70 value 1664.142208
## iter  80 value 1557.235427
## iter  90 value 1454.255077
## iter 100 value 1379.264502
## iter 110 value 1320.661067
## iter 120 value 1279.833239
## iter 130 value 1255.141347
## iter 140 value 1248.975435
## iter 150 value 1247.564827
## iter 160 value 1240.850835
## iter 170 value 1236.421566
## iter 180 value 1234.224291
## iter 190 value 1232.937590
## iter 200 value 1231.782910
## iter 210 value 1231.164421
## iter 220 value 1231.143689
## iter 230 value 1230.912328
## iter 240 value 1230.587594
## iter 250 value 1230.338139
## iter 260 value 1230.121232
## iter 270 value 1229.762269
## iter 280 value 1229.483677
## iter 290 value 1229.281246
## iter 300 value 1229.144030
## iter 310 value 1229.014965
## iter 320 value 1228.977646
## iter 330 value 1228.805885
## iter 340 value 1228.563171
## iter 350 value 1228.545104
## final  value 1228.540649 
## converged
## # weights:  71
## initial  value 1400456.740564 
## iter  10 value 3769.503148
## iter  20 value 1609.621536
## iter  30 value 1132.550349
## iter  40 value 961.110155
## iter  50 value 917.997930
## iter  60 value 862.524135
## iter  70 value 809.792333
## iter  80 value 780.232878
## iter  90 value 754.344792
## iter 100 value 738.271386
## iter 110 value 717.508006
## iter 120 value 695.799373
## iter 130 value 674.496613
## iter 140 value 630.817995
## iter 150 value 591.225520
## iter 160 value 576.693149
## iter 170 value 567.232258
## iter 180 value 565.322388
## iter 190 value 564.212179
## iter 200 value 563.463263
## iter 210 value 562.087923
## iter 220 value 561.191284
## iter 230 value 560.446339
## iter 240 value 560.171669
## iter 250 value 559.893370
## iter 260 value 559.576179
## iter 270 value 559.265503
## iter 280 value 558.987343
## iter 290 value 558.599411
## iter 300 value 557.590485
## iter 310 value 557.261948
## iter 320 value 556.330804
## iter 330 value 555.574914
## iter 340 value 555.550285
## iter 350 value 555.535896
## iter 360 value 555.505884
## final  value 555.482162 
## converged
## # weights:  106
## initial  value 1336939.781029 
## iter  10 value 1754.972796
## iter  20 value 1094.500757
## iter  30 value 936.752908
## iter  40 value 834.680610
## iter  50 value 750.982570
## iter  60 value 700.301408
## iter  70 value 651.567954
## iter  80 value 626.308397
## iter  90 value 585.727432
## iter 100 value 548.558212
## iter 110 value 516.426520
## iter 120 value 491.223989
## iter 130 value 479.461800
## iter 140 value 466.830406
## iter 150 value 450.233048
## iter 160 value 439.074105
## iter 170 value 429.181798
## iter 180 value 410.458859
## iter 190 value 401.622316
## iter 200 value 395.574096
## iter 210 value 391.355808
## iter 220 value 387.697246
## iter 230 value 386.481693
## iter 240 value 384.761524
## iter 250 value 381.761226
## iter 260 value 374.295363
## iter 270 value 367.077224
## iter 280 value 361.753023
## iter 290 value 355.441133
## iter 300 value 345.515320
## iter 310 value 339.600873
## iter 320 value 335.162353
## iter 330 value 333.390948
## iter 340 value 331.884928
## iter 350 value 330.990934
## iter 360 value 330.775421
## iter 370 value 330.127361
## iter 380 value 329.441873
## iter 390 value 329.128377
## iter 400 value 328.835852
## iter 410 value 328.569258
## iter 420 value 328.534451
## iter 430 value 328.390357
## iter 440 value 328.262871
## iter 450 value 328.168906
## iter 460 value 328.053485
## iter 470 value 328.010757
## iter 480 value 327.980793
## iter 490 value 327.918547
## iter 500 value 327.753972
## final  value 327.753972 
## stopped after 500 iterations
## # weights:  141
## initial  value 1374961.131929 
## iter  10 value 1831.333931
## iter  20 value 1100.759513
## iter  30 value 850.947399
## iter  40 value 734.750064
## iter  50 value 623.429004
## iter  60 value 572.232199
## iter  70 value 527.814731
## iter  80 value 492.226171
## iter  90 value 467.169913
## iter 100 value 442.971125
## iter 110 value 408.700256
## iter 120 value 376.701035
## iter 130 value 345.284574
## iter 140 value 322.484092
## iter 150 value 304.947307
## iter 160 value 289.332505
## iter 170 value 274.862248
## iter 180 value 266.611418
## iter 190 value 254.682939
## iter 200 value 248.400356
## iter 210 value 244.431939
## iter 220 value 241.297057
## iter 230 value 234.176118
## iter 240 value 227.629556
## iter 250 value 222.266938
## iter 260 value 216.766306
## iter 270 value 212.421305
## iter 280 value 209.641375
## iter 290 value 208.807716
## iter 300 value 208.443785
## iter 310 value 207.661347
## iter 320 value 206.788811
## iter 330 value 205.520869
## iter 340 value 204.476159
## iter 350 value 203.697017
## iter 360 value 201.833853
## iter 370 value 197.870000
## iter 380 value 195.701197
## iter 390 value 194.809276
## iter 400 value 194.326362
## iter 410 value 192.675713
## iter 420 value 189.786198
## iter 430 value 187.513462
## iter 440 value 186.848410
## iter 450 value 186.559953
## iter 460 value 186.393485
## iter 470 value 186.305587
## iter 480 value 185.760986
## iter 490 value 184.894006
## iter 500 value 184.529925
## final  value 184.529925 
## stopped after 500 iterations
## # weights:  15
## initial  value 1424983.074507 
## iter  10 value 3184.335134
## iter  20 value 1856.914509
## iter  30 value 1725.120665
## iter  40 value 1689.391178
## iter  50 value 1623.926095
## iter  60 value 1522.283189
## iter  70 value 1516.321418
## iter  80 value 1479.225989
## iter  90 value 1448.437455
## iter 100 value 1446.477076
## iter 110 value 1437.599280
## iter 120 value 1429.186688
## iter 130 value 1428.009786
## iter 140 value 1424.829034
## iter 150 value 1420.302768
## iter 160 value 1419.808244
## iter 170 value 1419.215739
## iter 180 value 1416.324401
## iter 190 value 1415.883355
## iter 200 value 1415.730854
## iter 210 value 1414.360844
## iter 220 value 1413.558667
## iter 230 value 1413.359859
## iter 240 value 1410.802805
## iter 250 value 1405.165636
## iter 260 value 1348.011689
## iter 270 value 1335.171944
## iter 280 value 1333.049343
## iter 290 value 1332.471431
## iter 300 value 1330.877991
## iter 310 value 1330.646715
## iter 320 value 1330.577360
## iter 330 value 1329.367964
## iter 340 value 1328.821836
## iter 350 value 1328.732937
## iter 360 value 1328.030514
## iter 370 value 1327.741318
## iter 380 value 1327.664792
## iter 390 value 1327.305892
## iter 400 value 1327.017540
## iter 410 value 1327.003829
## iter 420 value 1326.426259
## iter 430 value 1325.969052
## iter 440 value 1325.963325
## iter 450 value 1325.736175
## iter 460 value 1325.399859
## iter 470 value 1325.388595
## iter 480 value 1325.279220
## iter 490 value 1324.516932
## iter 500 value 1324.462657
## final  value 1324.462657 
## stopped after 500 iterations
## # weights:  36
## initial  value 1421277.795807 
## iter  10 value 15328.252597
## iter  20 value 6906.319879
## iter  30 value 3875.178882
## iter  40 value 1812.569604
## iter  50 value 1321.756770
## iter  60 value 1136.043716
## iter  70 value 1032.106810
## iter  80 value 1019.772689
## iter  90 value 1015.064454
## iter 100 value 995.723278
## iter 110 value 981.964414
## iter 120 value 973.206297
## iter 130 value 967.615874
## iter 140 value 963.991324
## iter 150 value 962.004563
## iter 160 value 961.965358
## iter 170 value 961.634283
## iter 180 value 961.358432
## iter 190 value 961.129678
## iter 200 value 960.241016
## iter 210 value 958.877263
## iter 220 value 957.420689
## iter 230 value 957.398436
## iter 240 value 957.360362
## iter 250 value 957.269326
## iter 260 value 957.204316
## iter 270 value 956.987902
## iter 280 value 956.829697
## iter 290 value 954.517657
## iter 300 value 954.037585
## iter 310 value 953.946375
## iter 320 value 953.563411
## iter 330 value 953.352571
## iter 340 value 953.318728
## iter 350 value 952.635606
## iter 360 value 952.249214
## iter 370 value 951.955832
## iter 380 value 951.943652
## iter 390 value 951.917819
## iter 400 value 951.819838
## iter 410 value 951.793436
## iter 420 value 951.721970
## iter 430 value 951.611241
## iter 440 value 951.548690
## iter 450 value 951.546125
## iter 450 value 951.546118
## iter 450 value 951.546118
## final  value 951.546118 
## converged
## # weights:  71
## initial  value 1366521.387012 
## iter  10 value 1350.201908
## iter  20 value 1016.016646
## iter  30 value 917.464908
## iter  40 value 870.256786
## iter  50 value 828.985060
## iter  60 value 799.745985
## iter  70 value 776.622199
## iter  80 value 751.315682
## iter  90 value 728.461335
## iter 100 value 698.342625
## iter 110 value 684.419145
## iter 120 value 676.521419
## iter 130 value 667.144859
## iter 140 value 661.673303
## iter 150 value 658.957310
## iter 160 value 655.500146
## iter 170 value 649.196530
## iter 180 value 643.154262
## iter 190 value 640.242539
## iter 200 value 635.569303
## iter 210 value 626.713164
## iter 220 value 612.017961
## iter 230 value 598.976373
## iter 240 value 588.704444
## iter 250 value 583.014381
## iter 260 value 577.745791
## iter 270 value 574.534050
## iter 280 value 573.606309
## iter 290 value 572.711124
## iter 300 value 572.486275
## iter 310 value 572.223319
## iter 320 value 572.122395
## iter 330 value 571.617871
## iter 340 value 569.738609
## iter 350 value 568.636824
## iter 360 value 568.270575
## iter 370 value 568.001508
## iter 380 value 567.830627
## iter 390 value 567.737005
## iter 400 value 567.647736
## iter 410 value 567.033922
## iter 420 value 559.613023
## iter 430 value 558.751537
## iter 440 value 558.702739
## iter 450 value 558.666222
## iter 460 value 558.609457
## iter 470 value 558.563013
## iter 480 value 558.447521
## iter 490 value 558.012973
## iter 500 value 557.754738
## final  value 557.754738 
## stopped after 500 iterations
## # weights:  106
## initial  value 1412090.511358 
## iter  10 value 1494.175724
## iter  20 value 1053.236614
## iter  30 value 926.095057
## iter  40 value 800.034680
## iter  50 value 740.946067
## iter  60 value 706.851837
## iter  70 value 673.778461
## iter  80 value 645.859827
## iter  90 value 599.567343
## iter 100 value 567.635545
## iter 110 value 542.638717
## iter 120 value 514.956111
## iter 130 value 495.128135
## iter 140 value 477.895883
## iter 150 value 460.157152
## iter 160 value 443.890560
## iter 170 value 428.705097
## iter 180 value 419.989287
## iter 190 value 414.454392
## iter 200 value 410.785414
## iter 210 value 406.183569
## iter 220 value 404.051716
## iter 230 value 402.095123
## iter 240 value 399.147165
## iter 250 value 395.577415
## iter 260 value 389.831574
## iter 270 value 385.342418
## iter 280 value 379.871588
## iter 290 value 373.750177
## iter 300 value 364.282784
## iter 310 value 356.193861
## iter 320 value 348.855263
## iter 330 value 342.435595
## iter 340 value 338.603993
## iter 350 value 336.808226
## iter 360 value 333.488791
## iter 370 value 329.659067
## iter 380 value 328.158702
## iter 390 value 326.845605
## iter 400 value 326.081922
## iter 410 value 325.660048
## iter 420 value 324.867680
## iter 430 value 324.446567
## iter 440 value 324.395327
## iter 450 value 324.309913
## iter 460 value 324.240130
## iter 470 value 324.131057
## iter 480 value 323.876405
## iter 490 value 323.477908
## iter 500 value 323.191407
## final  value 323.191407 
## stopped after 500 iterations
## # weights:  141
## initial  value 1435279.203886 
## iter  10 value 1863.870093
## iter  20 value 1006.341184
## iter  30 value 859.730613
## iter  40 value 735.984331
## iter  50 value 664.509105
## iter  60 value 612.967306
## iter  70 value 555.417067
## iter  80 value 506.920627
## iter  90 value 477.093636
## iter 100 value 459.829205
## iter 110 value 438.643502
## iter 120 value 405.989912
## iter 130 value 374.277064
## iter 140 value 341.918017
## iter 150 value 320.991637
## iter 160 value 304.919845
## iter 170 value 284.026379
## iter 180 value 271.731189
## iter 190 value 266.276569
## iter 200 value 256.866124
## iter 210 value 248.634888
## iter 220 value 240.094533
## iter 230 value 235.503319
## iter 240 value 231.726380
## iter 250 value 226.511500
## iter 260 value 219.410012
## iter 270 value 210.476832
## iter 280 value 202.949763
## iter 290 value 200.197208
## iter 300 value 198.200849
## iter 310 value 195.750135
## iter 320 value 193.207088
## iter 330 value 190.659507
## iter 340 value 188.951442
## iter 350 value 187.889466
## iter 360 value 186.311763
## iter 370 value 183.815835
## iter 380 value 180.897434
## iter 390 value 176.815870
## iter 400 value 173.410807
## iter 410 value 171.370605
## iter 420 value 168.742920
## iter 430 value 163.764221
## iter 440 value 159.835772
## iter 450 value 156.073842
## iter 460 value 153.724553
## iter 470 value 152.378150
## iter 480 value 151.592847
## iter 490 value 150.949857
## iter 500 value 150.180509
## final  value 150.180509 
## stopped after 500 iterations
## # weights:  15
## initial  value 1421937.842268 
## iter  10 value 9841.528333
## iter  20 value 6032.932905
## iter  30 value 5275.628985
## iter  40 value 4034.263567
## iter  50 value 2876.540130
## iter  60 value 2009.115668
## iter  70 value 1573.939919
## iter  80 value 1501.454923
## iter  90 value 1421.594909
## iter 100 value 1393.863434
## iter 110 value 1391.194916
## iter 120 value 1387.545279
## final  value 1387.510043 
## converged
## # weights:  36
## initial  value 1418151.550676 
## iter  10 value 51620.583755
## iter  20 value 9348.115332
## iter  30 value 6638.657634
## iter  40 value 3865.995839
## iter  50 value 2152.095467
## iter  60 value 1750.828106
## iter  70 value 1543.496365
## iter  80 value 1431.066410
## iter  90 value 1331.637340
## iter 100 value 1287.197586
## iter 110 value 1253.961821
## iter 120 value 1210.168873
## iter 130 value 1193.841768
## iter 140 value 1187.301892
## iter 150 value 1174.107301
## iter 160 value 1167.790235
## iter 170 value 1166.767672
## iter 180 value 1165.580066
## iter 190 value 1162.215981
## iter 200 value 1160.108590
## iter 210 value 1157.934453
## iter 220 value 1148.951332
## iter 230 value 1144.488777
## iter 240 value 1141.751269
## iter 250 value 1139.510403
## iter 260 value 1135.217482
## iter 270 value 1130.786250
## iter 280 value 1125.646633
## iter 290 value 1119.261157
## iter 300 value 1117.765979
## iter 310 value 1117.594749
## iter 320 value 1117.567856
## iter 330 value 1117.540571
## iter 340 value 1117.531894
## final  value 1117.531173 
## converged
## # weights:  71
## initial  value 1367285.629532 
## iter  10 value 4801.349716
## iter  20 value 3483.713084
## iter  30 value 3187.372938
## iter  40 value 2924.200310
## iter  50 value 2734.437484
## iter  60 value 2413.925923
## iter  70 value 2029.980447
## iter  80 value 1731.364718
## iter  90 value 1589.571834
## iter 100 value 1453.117213
## iter 110 value 1301.421105
## iter 120 value 1218.340091
## iter 130 value 1153.770736
## iter 140 value 1099.645962
## iter 150 value 1085.445809
## iter 160 value 1068.441741
## iter 170 value 1050.156267
## iter 180 value 1035.094906
## iter 190 value 1026.180947
## iter 200 value 1010.802956
## iter 210 value 999.352440
## iter 220 value 996.212885
## iter 230 value 986.084437
## iter 240 value 963.456190
## iter 250 value 945.103155
## iter 260 value 925.946221
## iter 270 value 909.731650
## iter 280 value 890.317120
## iter 290 value 882.789164
## iter 300 value 880.530894
## iter 310 value 878.775762
## iter 320 value 877.310864
## iter 330 value 876.421988
## iter 340 value 875.626201
## iter 350 value 853.830309
## iter 360 value 837.970615
## iter 370 value 832.484980
## iter 380 value 825.408212
## iter 390 value 820.984671
## iter 400 value 818.375632
## iter 410 value 813.446804
## iter 420 value 810.367794
## iter 430 value 809.730352
## iter 440 value 809.720850
## iter 450 value 809.708653
## iter 460 value 809.698730
## iter 470 value 809.695645
## iter 480 value 809.694909
## final  value 809.694894 
## converged
## # weights:  106
## initial  value 1383754.248993 
## iter  10 value 1442.611223
## iter  20 value 1074.695873
## iter  30 value 981.517233
## iter  40 value 933.462850
## iter  50 value 870.021077
## iter  60 value 813.550223
## iter  70 value 752.773095
## iter  80 value 717.092668
## iter  90 value 694.078603
## iter 100 value 676.380635
## iter 110 value 664.318065
## iter 120 value 655.471418
## iter 130 value 643.805697
## iter 140 value 638.775514
## iter 150 value 635.443607
## iter 160 value 633.323309
## iter 170 value 632.041302
## iter 180 value 630.902589
## iter 190 value 630.213338
## iter 200 value 629.539486
## iter 210 value 628.806289
## iter 220 value 628.632929
## iter 230 value 628.260194
## iter 240 value 627.541860
## iter 250 value 626.862333
## iter 260 value 626.241576
## iter 270 value 625.595962
## iter 280 value 625.207543
## iter 290 value 625.013305
## iter 300 value 624.854849
## iter 310 value 624.678406
## iter 320 value 624.597537
## iter 330 value 624.583958
## iter 340 value 624.562869
## iter 350 value 623.928459
## iter 360 value 622.410993
## iter 370 value 621.958179
## iter 380 value 621.611626
## iter 390 value 621.402205
## iter 400 value 621.370432
## iter 410 value 621.353866
## iter 420 value 621.349589
## final  value 621.349126 
## converged
## # weights:  141
## initial  value 1362691.192027 
## iter  10 value 1396.214450
## iter  20 value 1053.304048
## iter  30 value 970.142883
## iter  40 value 900.699605
## iter  50 value 855.719454
## iter  60 value 822.132898
## iter  70 value 791.549221
## iter  80 value 748.934954
## iter  90 value 718.493823
## iter 100 value 692.455716
## iter 110 value 672.134362
## iter 120 value 660.140616
## iter 130 value 653.144535
## iter 140 value 644.336068
## iter 150 value 632.411751
## iter 160 value 608.486967
## iter 170 value 585.289480
## iter 180 value 564.997697
## iter 190 value 557.686968
## iter 200 value 550.520342
## iter 210 value 545.267709
## iter 220 value 540.856281
## iter 230 value 538.662005
## iter 240 value 537.619865
## iter 250 value 536.843202
## iter 260 value 535.521756
## iter 270 value 534.366532
## iter 280 value 533.712529
## iter 290 value 533.256397
## iter 300 value 532.443802
## iter 310 value 530.825332
## iter 320 value 530.163911
## iter 330 value 529.882426
## iter 340 value 529.649631
## iter 350 value 529.438600
## iter 360 value 528.904547
## iter 370 value 527.564948
## iter 380 value 525.683209
## iter 390 value 524.802460
## iter 400 value 524.140795
## iter 410 value 521.752954
## iter 420 value 513.387863
## iter 430 value 510.366055
## iter 440 value 508.452656
## iter 450 value 508.061287
## iter 460 value 507.943478
## iter 470 value 507.838333
## iter 480 value 507.814377
## iter 490 value 507.813459
## final  value 507.813435 
## converged
## # weights:  15
## initial  value 1426595.928637 
## iter  10 value 17017.878547
## iter  20 value 9090.032534
## iter  30 value 7990.107122
## iter  40 value 3925.163419
## iter  50 value 2626.191477
## iter  60 value 1916.954235
## iter  70 value 1707.395862
## iter  80 value 1663.693129
## iter  90 value 1625.374557
## iter 100 value 1612.579832
## iter 110 value 1604.971906
## iter 120 value 1583.369547
## iter 130 value 1572.311308
## iter 140 value 1557.353843
## iter 150 value 1496.937542
## iter 160 value 1458.404851
## iter 170 value 1413.605562
## iter 180 value 1411.493109
## iter 190 value 1410.764512
## iter 200 value 1407.259326
## final  value 1406.632528 
## converged
## # weights:  36
## initial  value 1418371.926877 
## iter  10 value 18916.628757
## iter  20 value 14428.087708
## iter  30 value 8836.216035
## iter  40 value 6193.600532
## iter  50 value 4847.058241
## iter  60 value 2727.566199
## iter  70 value 1579.207455
## iter  80 value 1273.087030
## iter  90 value 1197.786573
## iter 100 value 1117.201581
## iter 110 value 1047.415165
## iter 120 value 1005.820174
## iter 130 value 987.439754
## iter 140 value 977.710448
## iter 150 value 971.741813
## iter 160 value 971.254451
## iter 170 value 968.825270
## iter 180 value 957.729166
## iter 190 value 954.491699
## iter 200 value 948.678479
## iter 210 value 945.305976
## iter 220 value 942.935557
## iter 230 value 942.672985
## iter 240 value 942.065239
## iter 250 value 940.850766
## iter 260 value 940.337084
## iter 270 value 939.787963
## iter 280 value 939.373664
## iter 290 value 938.209345
## iter 300 value 937.556138
## iter 310 value 937.070763
## iter 320 value 935.447503
## iter 330 value 929.205554
## iter 340 value 926.801094
## iter 350 value 925.060744
## iter 360 value 918.935071
## iter 370 value 915.602477
## iter 380 value 914.461503
## iter 390 value 910.673473
## iter 400 value 907.777216
## iter 410 value 905.679867
## iter 420 value 901.733043
## iter 430 value 900.424816
## iter 440 value 900.082796
## iter 450 value 900.073318
## iter 460 value 900.063941
## iter 470 value 900.018861
## iter 480 value 899.935805
## iter 490 value 899.919352
## final  value 899.917438 
## converged
## # weights:  71
## initial  value 1365783.165322 
## iter  10 value 1738.285767
## iter  20 value 1104.327619
## iter  30 value 1015.964016
## iter  40 value 951.741409
## iter  50 value 905.605703
## iter  60 value 863.722931
## iter  70 value 832.741110
## iter  80 value 797.180052
## iter  90 value 770.857187
## iter 100 value 754.168714
## iter 110 value 745.598271
## iter 120 value 729.551934
## iter 130 value 712.645514
## iter 140 value 702.237575
## iter 150 value 698.939193
## iter 160 value 696.619563
## iter 170 value 694.132101
## iter 180 value 690.023416
## iter 190 value 680.254605
## iter 200 value 659.659904
## iter 210 value 651.656002
## iter 220 value 648.377889
## iter 230 value 645.582393
## iter 240 value 642.540150
## iter 250 value 637.746077
## iter 260 value 634.558705
## iter 270 value 631.761105
## iter 280 value 629.477412
## iter 290 value 624.818697
## iter 300 value 623.458960
## iter 310 value 622.297201
## iter 320 value 620.900142
## iter 330 value 618.351214
## iter 340 value 614.775985
## iter 350 value 613.663686
## iter 360 value 606.214610
## iter 370 value 603.134759
## iter 380 value 598.219786
## iter 390 value 596.867761
## iter 400 value 596.753512
## iter 410 value 596.692617
## iter 420 value 596.681170
## iter 430 value 596.677448
## final  value 596.677417 
## converged
## # weights:  106
## initial  value 1403925.397441 
## iter  10 value 1158.820793
## iter  20 value 979.543542
## iter  30 value 869.469869
## iter  40 value 755.283825
## iter  50 value 689.990563
## iter  60 value 629.020080
## iter  70 value 551.568211
## iter  80 value 500.112668
## iter  90 value 464.391972
## iter 100 value 433.144624
## iter 110 value 407.061326
## iter 120 value 393.616337
## iter 130 value 386.575665
## iter 140 value 383.499837
## iter 150 value 379.150474
## iter 160 value 373.352734
## iter 170 value 369.839224
## iter 180 value 362.430979
## iter 190 value 358.079622
## iter 200 value 354.408505
## iter 210 value 349.313274
## iter 220 value 348.651988
## iter 230 value 348.137856
## iter 240 value 347.508687
## iter 250 value 346.585487
## iter 260 value 345.159527
## iter 270 value 343.842136
## iter 280 value 342.261962
## iter 290 value 337.157496
## iter 300 value 334.125030
## iter 310 value 330.622591
## iter 320 value 323.880588
## iter 330 value 320.028270
## iter 340 value 314.262211
## iter 350 value 311.993419
## iter 360 value 309.377885
## iter 370 value 305.523619
## iter 380 value 302.722307
## iter 390 value 302.345489
## iter 400 value 302.126253
## iter 410 value 302.006545
## iter 420 value 301.899817
## iter 430 value 301.807418
## iter 440 value 301.798523
## iter 450 value 301.782906
## iter 460 value 301.710917
## iter 470 value 301.539631
## iter 480 value 301.482744
## iter 490 value 301.467992
## iter 500 value 301.436826
## final  value 301.436826 
## stopped after 500 iterations
## # weights:  141
## initial  value 1344021.555710 
## iter  10 value 1410.599884
## iter  20 value 926.415518
## iter  30 value 813.455961
## iter  40 value 749.896152
## iter  50 value 659.731260
## iter  60 value 588.791864
## iter  70 value 543.294616
## iter  80 value 502.239566
## iter  90 value 460.619564
## iter 100 value 436.474295
## iter 110 value 414.571104
## iter 120 value 402.924335
## iter 130 value 388.478877
## iter 140 value 375.464065
## iter 150 value 358.204972
## iter 160 value 334.757560
## iter 170 value 309.829527
## iter 180 value 296.616766
## iter 190 value 287.413895
## iter 200 value 277.235181
## iter 210 value 263.464152
## iter 220 value 251.810705
## iter 230 value 244.993638
## iter 240 value 236.692107
## iter 250 value 231.480771
## iter 260 value 229.002418
## iter 270 value 226.954971
## iter 280 value 223.887127
## iter 290 value 222.682037
## iter 300 value 222.141484
## iter 310 value 221.513557
## iter 320 value 220.535741
## iter 330 value 219.745105
## iter 340 value 218.763501
## iter 350 value 217.383479
## iter 360 value 216.164420
## iter 370 value 214.455334
## iter 380 value 212.248506
## iter 390 value 210.390765
## iter 400 value 209.102367
## iter 410 value 208.324314
## iter 420 value 207.475027
## iter 430 value 206.985703
## iter 440 value 206.556325
## iter 450 value 206.044048
## iter 460 value 204.989411
## iter 470 value 203.886669
## iter 480 value 202.093646
## iter 490 value 198.507242
## iter 500 value 196.978472
## final  value 196.978472 
## stopped after 500 iterations
## # weights:  15
## initial  value 1403916.590347 
## iter  10 value 19631.327003
## iter  20 value 16658.481632
## iter  30 value 16357.930964
## iter  40 value 13489.768771
## iter  50 value 9235.516530
## iter  60 value 8847.106372
## iter  70 value 8675.084162
## iter  80 value 8675.023715
## iter  90 value 8674.837734
## iter 100 value 8666.193536
## iter 110 value 8346.851523
## iter 120 value 4204.635979
## iter 130 value 2839.711124
## iter 140 value 2155.922053
## iter 150 value 1792.889848
## iter 160 value 1513.329598
## iter 170 value 1414.277991
## iter 180 value 1403.415451
## iter 190 value 1392.581874
## iter 200 value 1381.641742
## iter 210 value 1379.197087
## iter 220 value 1379.000313
## iter 230 value 1376.017257
## iter 240 value 1375.433271
## final  value 1375.430641 
## converged
## # weights:  36
## initial  value 1375655.799795 
## iter  10 value 3675.595029
## iter  20 value 2812.122350
## iter  30 value 2095.996179
## iter  40 value 1496.622637
## iter  50 value 1230.828022
## iter  60 value 1055.970831
## iter  70 value 992.355653
## iter  80 value 964.616021
## iter  90 value 956.936073
## iter 100 value 956.420746
## iter 110 value 954.259685
## iter 120 value 936.403765
## iter 130 value 923.369730
## iter 140 value 897.956771
## iter 150 value 887.951429
## iter 160 value 877.847602
## iter 170 value 875.988514
## iter 180 value 875.008930
## iter 190 value 873.122649
## iter 200 value 870.607752
## iter 210 value 867.679898
## iter 220 value 864.595219
## iter 230 value 861.542821
## iter 240 value 860.353147
## iter 250 value 859.812511
## iter 260 value 858.301185
## iter 270 value 848.321841
## iter 280 value 844.811816
## iter 290 value 843.722455
## iter 300 value 836.885345
## iter 310 value 834.609278
## iter 320 value 829.204387
## iter 330 value 828.306526
## iter 340 value 821.917830
## iter 350 value 819.455267
## iter 360 value 814.099108
## iter 370 value 812.865546
## iter 380 value 812.593179
## iter 390 value 811.934537
## iter 400 value 811.205739
## iter 410 value 811.071141
## iter 420 value 810.037752
## final  value 809.999876 
## converged
## # weights:  71
## initial  value 1364099.852105 
## iter  10 value 1549.214879
## iter  20 value 1163.111642
## iter  30 value 984.281810
## iter  40 value 907.080865
## iter  50 value 818.234327
## iter  60 value 774.008498
## iter  70 value 755.853317
## iter  80 value 729.182097
## iter  90 value 714.981308
## iter 100 value 706.914316
## iter 110 value 695.220794
## iter 120 value 684.830197
## iter 130 value 680.630415
## iter 140 value 676.049513
## iter 150 value 674.225481
## iter 160 value 671.652645
## iter 170 value 667.223969
## iter 180 value 663.090889
## iter 190 value 656.625838
## iter 200 value 647.268827
## iter 210 value 636.756705
## iter 220 value 629.176017
## iter 230 value 623.755118
## iter 240 value 621.719629
## iter 250 value 619.376607
## iter 260 value 617.756779
## iter 270 value 615.603838
## iter 280 value 613.685388
## iter 290 value 612.423097
## iter 300 value 612.119647
## iter 310 value 611.851315
## iter 320 value 611.471378
## iter 330 value 610.863573
## iter 340 value 609.688212
## iter 350 value 608.175195
## iter 360 value 607.717942
## iter 370 value 607.064111
## iter 380 value 606.303775
## iter 390 value 605.629837
## iter 400 value 605.079362
## iter 410 value 604.941839
## iter 420 value 604.899361
## iter 430 value 604.860663
## iter 440 value 604.859673
## iter 450 value 604.854257
## iter 460 value 604.852340
## iter 470 value 604.848541
## iter 480 value 604.843834
## iter 490 value 604.833962
## iter 500 value 604.824480
## final  value 604.824480 
## stopped after 500 iterations
## # weights:  106
## initial  value 1395136.924542 
## iter  10 value 1416.602984
## iter  20 value 999.944528
## iter  30 value 874.796497
## iter  40 value 786.464772
## iter  50 value 749.288057
## iter  60 value 719.091860
## iter  70 value 663.744209
## iter  80 value 594.920985
## iter  90 value 569.146465
## iter 100 value 548.800527
## iter 110 value 524.730852
## iter 120 value 499.728507
## iter 130 value 466.207537
## iter 140 value 439.623954
## iter 150 value 424.524721
## iter 160 value 409.936479
## iter 170 value 395.981471
## iter 180 value 388.360415
## iter 190 value 382.614310
## iter 200 value 366.482340
## iter 210 value 356.111351
## iter 220 value 352.868844
## iter 230 value 351.301149
## iter 240 value 348.765047
## iter 250 value 346.870306
## iter 260 value 343.030534
## iter 270 value 337.174585
## iter 280 value 333.392901
## iter 290 value 328.267075
## iter 300 value 322.783923
## iter 310 value 318.275317
## iter 320 value 314.209133
## iter 330 value 312.003338
## iter 340 value 310.248088
## iter 350 value 305.685900
## iter 360 value 294.440305
## iter 370 value 291.853246
## iter 380 value 290.127231
## iter 390 value 289.754389
## iter 400 value 289.333872
## iter 410 value 288.992202
## iter 420 value 288.796912
## iter 430 value 288.752880
## iter 440 value 288.745847
## iter 450 value 288.728675
## iter 460 value 288.709766
## iter 470 value 288.696329
## iter 480 value 288.689396
## iter 490 value 288.684777
## iter 500 value 288.680206
## final  value 288.680206 
## stopped after 500 iterations
## # weights:  141
## initial  value 1406772.662054 
## iter  10 value 1714.478941
## iter  20 value 1036.595551
## iter  30 value 828.527235
## iter  40 value 737.638283
## iter  50 value 657.972671
## iter  60 value 573.349167
## iter  70 value 513.670059
## iter  80 value 458.568104
## iter  90 value 429.918241
## iter 100 value 399.396350
## iter 110 value 377.409826
## iter 120 value 357.143173
## iter 130 value 338.988724
## iter 140 value 326.856070
## iter 150 value 312.281079
## iter 160 value 297.027342
## iter 170 value 282.462774
## iter 180 value 271.572784
## iter 190 value 266.151141
## iter 200 value 260.343308
## iter 210 value 257.460322
## iter 220 value 255.927316
## iter 230 value 254.164418
## iter 240 value 252.485701
## iter 250 value 249.855468
## iter 260 value 247.799689
## iter 270 value 245.385211
## iter 280 value 242.811748
## iter 290 value 241.662311
## iter 300 value 241.193416
## iter 310 value 240.528705
## iter 320 value 239.702415
## iter 330 value 238.745557
## iter 340 value 236.760730
## iter 350 value 235.604664
## iter 360 value 233.970471
## iter 370 value 228.766464
## iter 380 value 223.061501
## iter 390 value 215.269419
## iter 400 value 210.188187
## iter 410 value 206.622860
## iter 420 value 202.827905
## iter 430 value 199.256243
## iter 440 value 197.269270
## iter 450 value 195.136679
## iter 460 value 193.950529
## iter 470 value 192.737697
## iter 480 value 191.669596
## iter 490 value 190.516192
## iter 500 value 189.397834
## final  value 189.397834 
## stopped after 500 iterations
## # weights:  15
## initial  value 1376674.377536 
## iter  10 value 5621.843818
## iter  20 value 5104.331639
## iter  30 value 4853.081946
## iter  40 value 4205.320797
## iter  50 value 3733.986549
## iter  60 value 3707.050660
## iter  70 value 3699.653566
## iter  80 value 3696.468391
## iter  90 value 3501.961455
## iter 100 value 2624.958484
## iter 110 value 1821.000497
## iter 120 value 1668.395432
## iter 130 value 1523.374573
## iter 140 value 1464.809582
## iter 150 value 1448.631549
## iter 160 value 1442.852875
## iter 170 value 1433.152033
## iter 180 value 1429.959829
## iter 190 value 1429.624015
## iter 200 value 1425.968961
## iter 210 value 1424.744834
## iter 220 value 1424.740764
## iter 220 value 1424.740753
## iter 220 value 1424.740746
## final  value 1424.740746 
## converged
## # weights:  36
## initial  value 1382691.125324 
## iter  10 value 9947.002188
## iter  20 value 5546.759036
## iter  30 value 3929.123987
## iter  40 value 2792.684034
## iter  50 value 2099.202778
## iter  60 value 1792.648877
## iter  70 value 1711.009906
## iter  80 value 1598.733324
## iter  90 value 1535.649846
## iter 100 value 1503.335612
## iter 110 value 1491.616080
## iter 120 value 1490.686889
## iter 130 value 1488.842807
## iter 140 value 1486.488297
## iter 150 value 1484.885798
## iter 160 value 1484.331113
## iter 170 value 1483.932454
## iter 180 value 1483.654989
## iter 190 value 1483.578155
## final  value 1483.577780 
## converged
## # weights:  71
## initial  value 1430181.745343 
## iter  10 value 1359.795027
## iter  20 value 1025.856086
## iter  30 value 906.949488
## iter  40 value 858.445484
## iter  50 value 821.349893
## iter  60 value 804.166791
## iter  70 value 786.964711
## iter  80 value 774.083679
## iter  90 value 759.624385
## iter 100 value 748.166369
## iter 110 value 731.474802
## iter 120 value 708.467501
## iter 130 value 692.361138
## iter 140 value 671.358734
## iter 150 value 647.852048
## iter 160 value 633.198745
## iter 170 value 622.539341
## iter 180 value 610.526348
## iter 190 value 587.406880
## iter 200 value 568.933425
## iter 210 value 557.365421
## iter 220 value 552.371952
## iter 230 value 545.880122
## iter 240 value 541.438773
## iter 250 value 537.443648
## iter 260 value 533.742008
## iter 270 value 529.072583
## iter 280 value 524.360143
## iter 290 value 520.624747
## iter 300 value 520.093739
## iter 310 value 519.648874
## iter 320 value 517.996409
## iter 330 value 515.616384
## iter 340 value 514.733478
## iter 350 value 512.284304
## iter 360 value 508.831928
## iter 370 value 501.317227
## iter 380 value 495.850960
## iter 390 value 495.584841
## iter 400 value 495.272253
## iter 410 value 494.583450
## iter 420 value 493.893843
## iter 430 value 487.262347
## iter 440 value 479.386878
## iter 450 value 476.195143
## iter 460 value 475.823571
## iter 470 value 474.834714
## iter 480 value 472.634940
## iter 490 value 472.133852
## iter 500 value 471.703563
## final  value 471.703563 
## stopped after 500 iterations
## # weights:  106
## initial  value 1388346.535000 
## iter  10 value 1878.003060
## iter  20 value 1036.019015
## iter  30 value 828.655095
## iter  40 value 722.873776
## iter  50 value 639.073632
## iter  60 value 576.470101
## iter  70 value 536.501313
## iter  80 value 502.909736
## iter  90 value 471.026188
## iter 100 value 452.279119
## iter 110 value 430.847087
## iter 120 value 409.072072
## iter 130 value 391.067232
## iter 140 value 367.668051
## iter 150 value 354.775451
## iter 160 value 350.715116
## iter 170 value 346.854163
## iter 180 value 342.895854
## iter 190 value 336.566826
## iter 200 value 331.410359
## iter 210 value 327.821525
## iter 220 value 326.444663
## iter 230 value 325.689413
## iter 240 value 323.952988
## iter 250 value 321.084134
## iter 260 value 317.119038
## iter 270 value 312.248271
## iter 280 value 309.532801
## iter 290 value 306.039480
## iter 300 value 298.926932
## iter 310 value 295.955197
## iter 320 value 292.231072
## iter 330 value 289.440307
## iter 340 value 287.694625
## iter 350 value 287.205264
## iter 360 value 286.361965
## iter 370 value 285.498696
## iter 380 value 284.909512
## iter 390 value 284.482997
## iter 400 value 284.180527
## iter 410 value 283.996566
## iter 420 value 283.909665
## iter 430 value 283.895038
## iter 440 value 283.893654
## iter 450 value 283.891687
## iter 460 value 283.888008
## iter 470 value 283.880113
## iter 480 value 283.875202
## iter 490 value 283.872609
## iter 500 value 283.863770
## final  value 283.863770 
## stopped after 500 iterations
## # weights:  141
## initial  value 1348136.615119 
## iter  10 value 1348.404639
## iter  20 value 1036.739033
## iter  30 value 912.863847
## iter  40 value 811.962117
## iter  50 value 678.852316
## iter  60 value 596.000188
## iter  70 value 540.074767
## iter  80 value 500.050711
## iter  90 value 437.829197
## iter 100 value 408.624370
## iter 110 value 391.100221
## iter 120 value 375.635385
## iter 130 value 354.295110
## iter 140 value 333.486763
## iter 150 value 310.830014
## iter 160 value 288.283947
## iter 170 value 274.981833
## iter 180 value 263.238701
## iter 190 value 254.531903
## iter 200 value 243.690175
## iter 210 value 235.071923
## iter 220 value 230.283509
## iter 230 value 224.077047
## iter 240 value 215.540120
## iter 250 value 207.781910
## iter 260 value 200.514124
## iter 270 value 198.047747
## iter 280 value 195.474576
## iter 290 value 194.581800
## iter 300 value 193.827179
## iter 310 value 192.737067
## iter 320 value 191.844497
## iter 330 value 190.354508
## iter 340 value 188.709876
## iter 350 value 186.701189
## iter 360 value 184.898027
## iter 370 value 183.576098
## iter 380 value 181.007497
## iter 390 value 177.261433
## iter 400 value 173.329196
## iter 410 value 170.711185
## iter 420 value 167.688026
## iter 430 value 165.320267
## iter 440 value 162.658250
## iter 450 value 160.712742
## iter 460 value 159.937916
## iter 470 value 159.337227
## iter 480 value 158.947436
## iter 490 value 157.987249
## iter 500 value 157.122143
## final  value 157.122143 
## stopped after 500 iterations
## # weights:  15
## initial  value 1427661.626729 
## iter  10 value 13793.275941
## iter  20 value 9502.033391
## iter  30 value 6797.213984
## iter  40 value 4628.285184
## iter  50 value 2349.210858
## iter  60 value 1766.809270
## iter  70 value 1765.220716
## iter  80 value 1757.092608
## iter  90 value 1754.417945
## iter 100 value 1753.521984
## iter 110 value 1752.527095
## iter 120 value 1751.923661
## iter 130 value 1751.858340
## iter 130 value 1751.858335
## final  value 1751.858194 
## converged
## # weights:  36
## initial  value 1377628.891625 
## iter  10 value 3470.726041
## iter  20 value 2442.297627
## iter  30 value 2244.466817
## iter  40 value 2037.517612
## iter  50 value 2012.806539
## iter  60 value 1995.958433
## iter  70 value 1747.585129
## iter  80 value 1536.446254
## iter  90 value 1401.129600
## iter 100 value 1369.280905
## iter 110 value 1339.565971
## iter 120 value 1263.680829
## iter 130 value 1226.559174
## iter 140 value 1197.998205
## iter 150 value 1178.277910
## iter 160 value 1099.061407
## iter 170 value 1056.115049
## iter 180 value 1030.073685
## iter 190 value 1023.941405
## iter 200 value 1021.349971
## iter 210 value 1012.492388
## iter 220 value 1008.327586
## iter 230 value 1000.741985
## iter 240 value 992.335732
## iter 250 value 977.904853
## iter 260 value 971.722418
## iter 270 value 971.104288
## iter 280 value 968.396919
## iter 290 value 965.322713
## iter 300 value 940.668319
## iter 310 value 935.363706
## iter 320 value 933.921653
## iter 330 value 933.426040
## iter 340 value 932.297846
## iter 350 value 931.460782
## iter 360 value 931.426861
## iter 370 value 931.241459
## iter 380 value 930.841915
## iter 390 value 929.712963
## iter 400 value 927.864537
## iter 410 value 926.110200
## iter 420 value 922.295063
## iter 430 value 921.912459
## iter 440 value 921.830175
## iter 450 value 921.455080
## iter 460 value 921.227791
## iter 470 value 921.132481
## iter 480 value 920.601806
## iter 490 value 920.344186
## iter 500 value 920.122982
## final  value 920.122982 
## stopped after 500 iterations
## # weights:  71
## initial  value 1441814.345653 
## iter  10 value 8964.553894
## iter  20 value 2697.397961
## iter  30 value 1642.198570
## iter  40 value 1315.795553
## iter  50 value 1142.534114
## iter  60 value 1055.405087
## iter  70 value 1000.562003
## iter  80 value 964.549049
## iter  90 value 930.033377
## iter 100 value 910.566422
## iter 110 value 880.577192
## iter 120 value 864.358830
## iter 130 value 857.540191
## iter 140 value 845.504482
## iter 150 value 841.733885
## iter 160 value 840.213392
## iter 170 value 838.995232
## iter 180 value 837.440665
## iter 190 value 833.878830
## iter 200 value 828.854726
## iter 210 value 822.670607
## iter 220 value 819.092311
## iter 230 value 816.523092
## iter 240 value 811.942242
## iter 250 value 805.927616
## iter 260 value 800.333793
## iter 270 value 793.226324
## iter 280 value 789.426458
## iter 290 value 787.449142
## iter 300 value 786.925715
## iter 310 value 785.736328
## iter 320 value 780.815440
## iter 330 value 768.760920
## iter 340 value 751.616856
## iter 350 value 747.376681
## iter 360 value 745.859932
## iter 370 value 744.337745
## iter 380 value 743.109826
## iter 390 value 742.009836
## iter 400 value 741.023646
## iter 410 value 739.921032
## iter 420 value 739.409954
## iter 430 value 739.286394
## iter 440 value 739.284262
## iter 450 value 739.283163
## iter 460 value 739.281257
## iter 470 value 739.278146
## iter 480 value 739.267116
## iter 490 value 739.244156
## iter 500 value 739.171731
## final  value 739.171731 
## stopped after 500 iterations
## # weights:  106
## initial  value 1434450.161944 
## iter  10 value 1277.871972
## iter  20 value 1017.304972
## iter  30 value 892.734701
## iter  40 value 800.416988
## iter  50 value 729.309155
## iter  60 value 679.817039
## iter  70 value 619.490133
## iter  80 value 578.488873
## iter  90 value 551.223548
## iter 100 value 531.676203
## iter 110 value 512.843881
## iter 120 value 494.357793
## iter 130 value 483.867872
## iter 140 value 467.021798
## iter 150 value 443.632608
## iter 160 value 423.438251
## iter 170 value 413.895787
## iter 180 value 393.362818
## iter 190 value 378.415858
## iter 200 value 367.661738
## iter 210 value 354.212486
## iter 220 value 349.835078
## iter 230 value 347.179848
## iter 240 value 343.244367
## iter 250 value 339.776803
## iter 260 value 334.620681
## iter 270 value 331.547502
## iter 280 value 328.774982
## iter 290 value 326.798326
## iter 300 value 321.529210
## iter 310 value 318.370870
## iter 320 value 314.113507
## iter 330 value 306.837927
## iter 340 value 299.641913
## iter 350 value 295.992983
## iter 360 value 293.938729
## iter 370 value 292.628408
## iter 380 value 291.184495
## iter 390 value 290.785342
## iter 400 value 290.666830
## iter 410 value 290.474187
## iter 420 value 290.382428
## iter 430 value 290.354936
## iter 440 value 290.352191
## iter 450 value 290.345442
## iter 460 value 290.339592
## iter 470 value 290.332895
## iter 480 value 290.309380
## iter 490 value 290.227195
## iter 500 value 290.114493
## final  value 290.114493 
## stopped after 500 iterations
## # weights:  141
## initial  value 1412825.912914 
## iter  10 value 1622.287227
## iter  20 value 1027.175289
## iter  30 value 933.843782
## iter  40 value 816.994609
## iter  50 value 728.817589
## iter  60 value 668.545782
## iter  70 value 612.028895
## iter  80 value 561.048547
## iter  90 value 519.495084
## iter 100 value 483.047482
## iter 110 value 453.567890
## iter 120 value 422.274667
## iter 130 value 402.452536
## iter 140 value 363.881929
## iter 150 value 342.392514
## iter 160 value 323.615831
## iter 170 value 313.252920
## iter 180 value 300.711601
## iter 190 value 289.230853
## iter 200 value 279.521352
## iter 210 value 267.254278
## iter 220 value 262.369089
## iter 230 value 253.680932
## iter 240 value 246.146566
## iter 250 value 236.879230
## iter 260 value 227.189029
## iter 270 value 219.682763
## iter 280 value 212.605946
## iter 290 value 209.810365
## iter 300 value 207.865112
## iter 310 value 204.917534
## iter 320 value 202.974789
## iter 330 value 200.721545
## iter 340 value 198.647181
## iter 350 value 197.305454
## iter 360 value 195.876452
## iter 370 value 192.568266
## iter 380 value 190.440122
## iter 390 value 187.374904
## iter 400 value 183.581968
## iter 410 value 180.260881
## iter 420 value 177.493354
## iter 430 value 174.679135
## iter 440 value 173.046095
## iter 450 value 172.056058
## iter 460 value 171.588915
## iter 470 value 171.196504
## iter 480 value 170.993620
## iter 490 value 170.795823
## iter 500 value 170.050674
## final  value 170.050674 
## stopped after 500 iterations
## # weights:  15
## initial  value 1408986.934906 
## iter  10 value 20985.853377
## iter  20 value 18459.320081
## iter  30 value 14466.829187
## iter  40 value 10360.851669
## iter  50 value 6077.681780
## iter  60 value 3193.120858
## iter  70 value 2710.417361
## iter  80 value 2015.950262
## iter  90 value 1944.507555
## iter 100 value 1944.268906
## final  value 1943.770161 
## converged
## # weights:  36
## initial  value 1424952.463072 
## iter  10 value 5181.022819
## iter  20 value 2990.784576
## iter  30 value 2146.527377
## iter  40 value 1792.535196
## iter  50 value 1679.140131
## iter  60 value 1482.584543
## iter  70 value 1407.448429
## iter  80 value 1363.953319
## iter  90 value 1309.425001
## iter 100 value 1281.526493
## iter 110 value 1277.619300
## iter 120 value 1276.951650
## iter 130 value 1275.365347
## iter 140 value 1275.113500
## final  value 1275.112288 
## converged
## # weights:  71
## initial  value 1429060.172372 
## iter  10 value 1418.147438
## iter  20 value 1193.100841
## iter  30 value 1138.425928
## iter  40 value 1109.584556
## iter  50 value 1080.077597
## iter  60 value 1035.973241
## iter  70 value 974.134717
## iter  80 value 938.002297
## iter  90 value 918.991655
## iter 100 value 910.385064
## iter 110 value 897.278739
## iter 120 value 880.451020
## iter 130 value 871.325790
## iter 140 value 867.609328
## iter 150 value 866.531714
## iter 160 value 864.909898
## iter 170 value 862.806475
## iter 180 value 860.059381
## iter 190 value 852.411528
## iter 200 value 843.481627
## iter 210 value 839.922647
## iter 220 value 837.711992
## iter 230 value 831.224292
## iter 240 value 827.610699
## iter 250 value 821.150431
## iter 260 value 818.464343
## iter 270 value 815.260980
## iter 280 value 811.319243
## iter 290 value 805.509097
## iter 300 value 804.189739
## iter 310 value 802.524599
## iter 320 value 798.877375
## iter 330 value 794.785623
## iter 340 value 790.469847
## iter 350 value 787.312113
## iter 360 value 786.128768
## iter 370 value 785.684914
## iter 380 value 785.606827
## final  value 785.602927 
## converged
## # weights:  106
## initial  value 1457393.662670 
## iter  10 value 1557.199836
## iter  20 value 1134.818062
## iter  30 value 999.912513
## iter  40 value 951.481670
## iter  50 value 897.201569
## iter  60 value 869.302980
## iter  70 value 826.885783
## iter  80 value 802.474842
## iter  90 value 777.673778
## iter 100 value 760.187391
## iter 110 value 742.766858
## iter 120 value 727.423780
## iter 130 value 718.035738
## iter 140 value 702.102235
## iter 150 value 684.594146
## iter 160 value 677.945601
## iter 170 value 669.492198
## iter 180 value 664.894441
## iter 190 value 662.344934
## iter 200 value 660.965928
## iter 210 value 658.859020
## iter 220 value 657.831002
## iter 230 value 656.324306
## iter 240 value 653.945843
## iter 250 value 650.616812
## iter 260 value 647.373679
## iter 270 value 644.620580
## iter 280 value 642.318794
## iter 290 value 640.374353
## iter 300 value 637.514473
## iter 310 value 624.212510
## iter 320 value 617.611809
## iter 330 value 615.647337
## iter 340 value 614.290170
## iter 350 value 608.560098
## iter 360 value 597.891043
## iter 370 value 595.209494
## iter 380 value 593.827338
## iter 390 value 593.094177
## iter 400 value 592.987570
## iter 410 value 592.965640
## iter 420 value 592.960258
## final  value 592.959782 
## converged
## # weights:  141
## initial  value 1400810.069917 
## iter  10 value 1799.311970
## iter  20 value 1303.511605
## iter  30 value 1027.560078
## iter  40 value 949.153520
## iter  50 value 874.085266
## iter  60 value 844.898199
## iter  70 value 809.437515
## iter  80 value 765.205522
## iter  90 value 746.127091
## iter 100 value 730.707761
## iter 110 value 718.157413
## iter 120 value 704.947451
## iter 130 value 694.985050
## iter 140 value 689.156792
## iter 150 value 683.970761
## iter 160 value 679.695849
## iter 170 value 675.859216
## iter 180 value 668.104260
## iter 190 value 651.416306
## iter 200 value 637.134357
## iter 210 value 625.783503
## iter 220 value 616.704054
## iter 230 value 608.045919
## iter 240 value 600.718818
## iter 250 value 595.242235
## iter 260 value 592.704917
## iter 270 value 590.925946
## iter 280 value 587.539662
## iter 290 value 583.880277
## iter 300 value 581.413736
## iter 310 value 575.973352
## iter 320 value 573.297888
## iter 330 value 571.296281
## iter 340 value 570.311583
## iter 350 value 568.923493
## iter 360 value 568.118303
## iter 370 value 566.588831
## iter 380 value 561.634314
## iter 390 value 560.163042
## iter 400 value 558.877240
## iter 410 value 557.867369
## iter 420 value 556.428619
## iter 430 value 555.584055
## iter 440 value 555.071903
## iter 450 value 554.859104
## iter 460 value 554.696828
## iter 470 value 554.690647
## final  value 554.690363 
## converged
## # weights:  15
## initial  value 1447346.466397 
## iter  10 value 9299.894054
## iter  20 value 3516.575673
## iter  30 value 2092.860369
## iter  40 value 1858.505191
## iter  50 value 1770.138144
## iter  60 value 1740.290535
## iter  70 value 1710.183812
## iter  80 value 1527.970253
## iter  90 value 1493.693263
## iter 100 value 1292.222793
## iter 110 value 1196.664669
## iter 120 value 1186.691979
## iter 130 value 1180.942644
## iter 140 value 1179.700850
## iter 150 value 1178.903426
## iter 160 value 1178.038924
## iter 170 value 1176.034017
## iter 180 value 1175.117315
## iter 190 value 1174.476149
## iter 200 value 1174.196668
## iter 210 value 1174.193115
## final  value 1174.192520 
## converged
## # weights:  36
## initial  value 1356986.625888 
## iter  10 value 7074.598401
## iter  20 value 2459.361402
## iter  30 value 1731.668905
## iter  40 value 1193.694142
## iter  50 value 1037.538754
## iter  60 value 1004.325158
## iter  70 value 994.277430
## iter  80 value 990.472406
## iter  90 value 988.878079
## iter 100 value 982.039727
## iter 110 value 970.323716
## iter 120 value 955.247332
## iter 130 value 944.909823
## iter 140 value 931.231258
## iter 150 value 912.187056
## iter 160 value 901.647812
## iter 170 value 895.922190
## iter 180 value 889.055226
## iter 190 value 878.372436
## iter 200 value 873.199637
## iter 210 value 870.405637
## iter 220 value 869.984116
## iter 230 value 869.918142
## iter 240 value 869.824494
## iter 250 value 869.746318
## iter 260 value 869.403499
## iter 270 value 869.397973
## final  value 869.397452 
## converged
## # weights:  71
## initial  value 1417800.901559 
## iter  10 value 1740.270332
## iter  20 value 1158.517677
## iter  30 value 1023.205752
## iter  40 value 926.083079
## iter  50 value 868.726272
## iter  60 value 825.349074
## iter  70 value 810.341172
## iter  80 value 751.334259
## iter  90 value 730.042979
## iter 100 value 697.275690
## iter 110 value 675.242850
## iter 120 value 656.822588
## iter 130 value 642.764232
## iter 140 value 622.291473
## iter 150 value 602.874378
## iter 160 value 592.151162
## iter 170 value 578.676084
## iter 180 value 561.857170
## iter 190 value 544.419308
## iter 200 value 534.891970
## iter 210 value 526.091616
## iter 220 value 517.149032
## iter 230 value 506.397388
## iter 240 value 502.883014
## iter 250 value 500.957554
## iter 260 value 498.713438
## iter 270 value 496.422961
## iter 280 value 494.281133
## iter 290 value 493.367401
## iter 300 value 493.171700
## iter 310 value 492.897882
## iter 320 value 492.590265
## iter 330 value 492.083835
## iter 340 value 490.771951
## iter 350 value 489.543882
## iter 360 value 489.238281
## iter 370 value 488.988331
## iter 380 value 488.937489
## iter 390 value 488.899004
## iter 400 value 488.888576
## iter 410 value 488.885926
## iter 420 value 488.885692
## final  value 488.885685 
## converged
## # weights:  106
## initial  value 1374480.931723 
## iter  10 value 1207.326961
## iter  20 value 1007.469962
## iter  30 value 895.488828
## iter  40 value 815.740049
## iter  50 value 752.202782
## iter  60 value 713.940912
## iter  70 value 662.738635
## iter  80 value 634.618804
## iter  90 value 614.331922
## iter 100 value 595.287286
## iter 110 value 578.044096
## iter 120 value 558.157726
## iter 130 value 541.312234
## iter 140 value 521.811302
## iter 150 value 499.493602
## iter 160 value 479.683491
## iter 170 value 464.004036
## iter 180 value 454.180065
## iter 190 value 446.839690
## iter 200 value 438.171123
## iter 210 value 423.998131
## iter 220 value 418.390025
## iter 230 value 415.771044
## iter 240 value 411.617170
## iter 250 value 403.315964
## iter 260 value 395.170007
## iter 270 value 388.060051
## iter 280 value 383.297416
## iter 290 value 378.403069
## iter 300 value 374.275601
## iter 310 value 370.498740
## iter 320 value 367.213614
## iter 330 value 366.268632
## iter 340 value 365.356791
## iter 350 value 365.140489
## iter 360 value 365.096208
## iter 370 value 365.086513
## iter 380 value 365.085364
## iter 390 value 365.085101
## final  value 365.085093 
## converged
## # weights:  141
## initial  value 1457144.428539 
## iter  10 value 1357.938142
## iter  20 value 1056.045132
## iter  30 value 925.175302
## iter  40 value 812.813513
## iter  50 value 750.331680
## iter  60 value 707.926008
## iter  70 value 653.277319
## iter  80 value 582.687107
## iter  90 value 536.159823
## iter 100 value 487.922223
## iter 110 value 458.811011
## iter 120 value 425.335333
## iter 130 value 388.791907
## iter 140 value 338.847453
## iter 150 value 320.730838
## iter 160 value 312.505654
## iter 170 value 298.349396
## iter 180 value 291.572209
## iter 190 value 280.357549
## iter 200 value 266.562766
## iter 210 value 254.535847
## iter 220 value 244.178157
## iter 230 value 237.643888
## iter 240 value 231.857390
## iter 250 value 224.345362
## iter 260 value 220.689994
## iter 270 value 218.606895
## iter 280 value 217.072419
## iter 290 value 216.532138
## iter 300 value 215.999574
## iter 310 value 215.285988
## iter 320 value 214.318708
## iter 330 value 212.310094
## iter 340 value 210.234280
## iter 350 value 208.193171
## iter 360 value 207.168231
## iter 370 value 206.143178
## iter 380 value 205.280567
## iter 390 value 204.495536
## iter 400 value 203.580698
## iter 410 value 202.896600
## iter 420 value 202.034853
## iter 430 value 200.754908
## iter 440 value 199.913948
## iter 450 value 199.467116
## iter 460 value 199.206448
## iter 470 value 198.699061
## iter 480 value 194.732367
## iter 490 value 191.657804
## iter 500 value 189.040110
## final  value 189.040110 
## stopped after 500 iterations
## # weights:  15
## initial  value 1420233.231794 
## iter  10 value 72824.933060
## iter  20 value 7686.083658
## iter  30 value 2585.826051
## iter  40 value 1889.052754
## iter  50 value 1818.810222
## iter  60 value 1769.759746
## iter  70 value 1759.660572
## iter  80 value 1758.303113
## iter  90 value 1754.949232
## iter 100 value 1754.033414
## iter 110 value 1749.794866
## iter 120 value 1748.930384
## iter 130 value 1747.485461
## iter 140 value 1741.655083
## iter 150 value 1737.890747
## iter 160 value 1737.380249
## iter 170 value 1734.990023
## iter 180 value 1730.467206
## iter 190 value 1701.748252
## iter 200 value 1657.638733
## iter 210 value 1611.231712
## iter 220 value 1347.596525
## iter 230 value 1247.001076
## iter 240 value 1220.440842
## iter 250 value 1187.254709
## iter 260 value 1180.289545
## iter 270 value 1180.142354
## iter 280 value 1179.820896
## iter 290 value 1179.351309
## iter 300 value 1179.318396
## iter 310 value 1179.104688
## iter 320 value 1178.443368
## iter 330 value 1178.217280
## iter 340 value 1177.976718
## iter 350 value 1177.483910
## iter 360 value 1177.145903
## iter 370 value 1176.872470
## iter 380 value 1175.771875
## iter 390 value 1174.936143
## iter 400 value 1174.020837
## iter 410 value 1172.296616
## iter 420 value 1171.803909
## iter 430 value 1171.710385
## iter 440 value 1171.630748
## final  value 1171.626535 
## converged
## # weights:  36
## initial  value 1377478.536983 
## iter  10 value 8245.875436
## iter  20 value 3072.328775
## iter  30 value 1894.546374
## iter  40 value 1383.894150
## iter  50 value 1149.882290
## iter  60 value 1086.575855
## iter  70 value 1041.940879
## iter  80 value 1037.713885
## iter  90 value 1028.718193
## iter 100 value 1013.551115
## iter 110 value 1000.828098
## iter 120 value 968.192258
## iter 130 value 948.528103
## iter 140 value 932.576946
## iter 150 value 913.320604
## iter 160 value 905.395123
## iter 170 value 898.727362
## iter 180 value 889.982531
## iter 190 value 886.226974
## iter 200 value 884.740083
## iter 210 value 883.939910
## iter 220 value 883.236821
## iter 230 value 883.144026
## iter 240 value 883.072724
## iter 250 value 882.818938
## iter 260 value 882.051527
## iter 270 value 881.412403
## iter 280 value 880.500545
## iter 290 value 879.603864
## iter 300 value 879.425685
## iter 310 value 879.402045
## iter 320 value 879.365995
## iter 330 value 879.335461
## iter 340 value 879.318525
## iter 350 value 879.267244
## iter 360 value 879.262811
## iter 370 value 879.218831
## iter 380 value 879.217606
## iter 390 value 879.212485
## iter 400 value 879.093072
## iter 410 value 879.062739
## iter 420 value 878.453998
## iter 430 value 876.135221
## iter 440 value 868.179758
## iter 450 value 862.467991
## iter 460 value 860.631476
## iter 470 value 859.901115
## iter 480 value 859.894583
## iter 490 value 859.882043
## iter 490 value 859.882041
## final  value 859.882041 
## converged
## # weights:  71
## initial  value 1414148.933316 
## iter  10 value 1639.009149
## iter  20 value 1119.523925
## iter  30 value 1018.934173
## iter  40 value 943.330056
## iter  50 value 898.298762
## iter  60 value 875.962676
## iter  70 value 852.757234
## iter  80 value 822.678789
## iter  90 value 786.678396
## iter 100 value 754.872652
## iter 110 value 726.064866
## iter 120 value 711.487305
## iter 130 value 696.580867
## iter 140 value 680.561011
## iter 150 value 671.881421
## iter 160 value 667.892477
## iter 170 value 659.283581
## iter 180 value 650.022477
## iter 190 value 641.495508
## iter 200 value 631.701145
## iter 210 value 607.242805
## iter 220 value 579.029365
## iter 230 value 567.176955
## iter 240 value 559.866760
## iter 250 value 556.780081
## iter 260 value 555.669176
## iter 270 value 554.578424
## iter 280 value 553.665928
## iter 290 value 553.381924
## iter 300 value 553.343726
## iter 310 value 553.298412
## iter 320 value 553.268264
## iter 330 value 553.192785
## iter 340 value 553.129840
## iter 350 value 552.115042
## iter 360 value 552.036909
## iter 370 value 550.997426
## iter 380 value 550.801109
## iter 390 value 550.775383
## final  value 550.735265 
## converged
## # weights:  106
## initial  value 1453336.037855 
## iter  10 value 1820.586956
## iter  20 value 1078.222267
## iter  30 value 940.487328
## iter  40 value 833.753838
## iter  50 value 749.525527
## iter  60 value 697.302406
## iter  70 value 664.434318
## iter  80 value 640.350711
## iter  90 value 623.931378
## iter 100 value 608.760205
## iter 110 value 591.108640
## iter 120 value 551.379667
## iter 130 value 512.613586
## iter 140 value 475.005600
## iter 150 value 459.523438
## iter 160 value 450.505368
## iter 170 value 441.622809
## iter 180 value 432.006703
## iter 190 value 424.805599
## iter 200 value 419.669779
## iter 210 value 414.660947
## iter 220 value 413.318250
## iter 230 value 412.877222
## iter 240 value 412.230969
## iter 250 value 410.796899
## iter 260 value 408.643973
## iter 270 value 406.082772
## iter 280 value 403.208333
## iter 290 value 400.556846
## iter 300 value 397.375323
## iter 310 value 392.567862
## iter 320 value 388.909083
## iter 330 value 386.790814
## iter 340 value 385.907383
## iter 350 value 382.876547
## iter 360 value 380.475740
## iter 370 value 379.114179
## iter 380 value 377.940263
## iter 390 value 377.480319
## iter 400 value 377.106174
## iter 410 value 376.539383
## iter 420 value 376.330141
## iter 430 value 376.276542
## iter 440 value 376.269503
## iter 450 value 376.262611
## iter 460 value 376.243786
## iter 470 value 376.206031
## iter 480 value 376.124653
## iter 490 value 375.977379
## iter 500 value 375.806386
## final  value 375.806386 
## stopped after 500 iterations
## # weights:  141
## initial  value 1356386.438590 
## iter  10 value 1506.888527
## iter  20 value 978.485610
## iter  30 value 826.714424
## iter  40 value 738.397106
## iter  50 value 667.866788
## iter  60 value 612.575851
## iter  70 value 573.927537
## iter  80 value 553.403119
## iter  90 value 526.342407
## iter 100 value 488.142545
## iter 110 value 454.814186
## iter 120 value 431.521530
## iter 130 value 420.199413
## iter 140 value 408.194922
## iter 150 value 393.920237
## iter 160 value 380.075578
## iter 170 value 362.301148
## iter 180 value 337.879537
## iter 190 value 319.092985
## iter 200 value 305.086712
## iter 210 value 289.436610
## iter 220 value 275.282778
## iter 230 value 263.088370
## iter 240 value 255.861135
## iter 250 value 250.041495
## iter 260 value 243.849243
## iter 270 value 236.090546
## iter 280 value 230.140358
## iter 290 value 227.886461
## iter 300 value 226.342697
## iter 310 value 224.425217
## iter 320 value 220.846661
## iter 330 value 217.265587
## iter 340 value 210.726473
## iter 350 value 205.568968
## iter 360 value 198.844698
## iter 370 value 191.602181
## iter 380 value 187.115342
## iter 390 value 183.725270
## iter 400 value 179.301109
## iter 410 value 176.980711
## iter 420 value 172.813455
## iter 430 value 169.147296
## iter 440 value 166.695214
## iter 450 value 165.032692
## iter 460 value 162.842445
## iter 470 value 161.621900
## iter 480 value 160.932510
## iter 490 value 160.140090
## iter 500 value 159.626919
## final  value 159.626919 
## stopped after 500 iterations
## # weights:  15
## initial  value 1384763.883320 
## iter  10 value 7709.613116
## iter  20 value 5823.399736
## iter  30 value 5774.511454
## iter  40 value 5430.634202
## iter  50 value 3748.385141
## iter  60 value 2172.464199
## iter  70 value 1835.834953
## iter  80 value 1797.941458
## iter  90 value 1761.069461
## iter 100 value 1755.193933
## iter 110 value 1754.084052
## iter 120 value 1753.378876
## iter 130 value 1752.814657
## iter 140 value 1752.357952
## final  value 1752.356589 
## converged
## # weights:  36
## initial  value 1433335.448558 
## iter  10 value 42664.911351
## iter  20 value 11963.604967
## iter  30 value 5739.942922
## iter  40 value 4903.331476
## iter  50 value 2780.652397
## iter  60 value 2318.589688
## iter  70 value 1958.132627
## iter  80 value 1795.713278
## iter  90 value 1706.002418
## iter 100 value 1544.410946
## iter 110 value 1534.591106
## iter 120 value 1533.024883
## iter 130 value 1493.273538
## iter 140 value 1473.025962
## iter 150 value 1470.725067
## iter 160 value 1469.965157
## iter 170 value 1465.845417
## iter 180 value 1463.578753
## iter 190 value 1463.555731
## iter 200 value 1463.485148
## iter 210 value 1463.013525
## iter 220 value 1461.948810
## iter 230 value 1461.276859
## iter 240 value 1460.677282
## iter 250 value 1460.545000
## iter 260 value 1460.499188
## iter 270 value 1460.494519
## iter 280 value 1460.468861
## final  value 1460.468795 
## converged
## # weights:  71
## initial  value 1401681.506208 
## iter  10 value 2854.215405
## iter  20 value 1487.814304
## iter  30 value 1201.871973
## iter  40 value 1024.282208
## iter  50 value 888.161231
## iter  60 value 803.317577
## iter  70 value 727.537760
## iter  80 value 689.000959
## iter  90 value 673.970894
## iter 100 value 657.883733
## iter 110 value 640.746142
## iter 120 value 628.300597
## iter 130 value 618.870330
## iter 140 value 615.201054
## iter 150 value 613.206542
## iter 160 value 612.776573
## iter 170 value 610.883169
## iter 180 value 607.158440
## iter 190 value 597.743822
## iter 200 value 588.247183
## iter 210 value 581.756431
## iter 220 value 575.689537
## iter 230 value 569.999619
## iter 240 value 567.308965
## iter 250 value 564.315996
## iter 260 value 562.268010
## iter 270 value 561.902758
## iter 280 value 561.832860
## iter 290 value 561.814868
## iter 300 value 561.811727
## iter 310 value 561.808621
## iter 320 value 561.805016
## iter 330 value 561.797760
## iter 340 value 561.782765
## iter 350 value 561.725533
## iter 360 value 561.664280
## iter 370 value 561.563971
## iter 380 value 561.471524
## iter 390 value 561.393688
## iter 400 value 561.349385
## iter 410 value 561.328545
## iter 420 value 561.311099
## iter 430 value 561.273777
## iter 440 value 561.272772
## iter 450 value 561.271119
## iter 460 value 561.267529
## iter 470 value 561.260158
## iter 480 value 561.242793
## iter 490 value 561.214571
## iter 500 value 561.203714
## final  value 561.203714 
## stopped after 500 iterations
## # weights:  106
## initial  value 1449116.096592 
## iter  10 value 3524.168720
## iter  20 value 1706.056434
## iter  30 value 1175.241569
## iter  40 value 949.164296
## iter  50 value 800.218551
## iter  60 value 714.812143
## iter  70 value 654.456917
## iter  80 value 604.845858
## iter  90 value 575.604733
## iter 100 value 552.360200
## iter 110 value 520.383974
## iter 120 value 496.063929
## iter 130 value 481.476909
## iter 140 value 475.005205
## iter 150 value 470.704458
## iter 160 value 463.748575
## iter 170 value 458.204308
## iter 180 value 452.466570
## iter 190 value 448.152740
## iter 200 value 444.498272
## iter 210 value 441.570824
## iter 220 value 440.726666
## iter 230 value 439.303303
## iter 240 value 437.816046
## iter 250 value 435.474563
## iter 260 value 432.000325
## iter 270 value 427.115436
## iter 280 value 425.128583
## iter 290 value 423.514197
## iter 300 value 421.094465
## iter 310 value 418.586113
## iter 320 value 416.935557
## iter 330 value 415.238200
## iter 340 value 413.329691
## iter 350 value 411.762591
## iter 360 value 410.146748
## iter 370 value 408.773403
## iter 380 value 408.274991
## iter 390 value 407.680032
## iter 400 value 407.150610
## iter 410 value 406.649517
## iter 420 value 405.060350
## iter 430 value 403.674625
## iter 440 value 403.558472
## iter 450 value 403.372627
## iter 460 value 403.161878
## iter 470 value 402.866513
## iter 480 value 402.580088
## iter 490 value 402.212321
## iter 500 value 401.966677
## final  value 401.966677 
## stopped after 500 iterations
## # weights:  141
## initial  value 1462307.700241 
## iter  10 value 1476.895771
## iter  20 value 1063.115693
## iter  30 value 921.095303
## iter  40 value 771.355034
## iter  50 value 671.327010
## iter  60 value 613.877943
## iter  70 value 569.649365
## iter  80 value 499.171489
## iter  90 value 436.780669
## iter 100 value 390.221767
## iter 110 value 363.031769
## iter 120 value 349.958291
## iter 130 value 338.693274
## iter 140 value 323.473449
## iter 150 value 306.922440
## iter 160 value 296.666199
## iter 170 value 288.678084
## iter 180 value 277.862885
## iter 190 value 267.448201
## iter 200 value 260.127440
## iter 210 value 253.774820
## iter 220 value 249.223254
## iter 230 value 245.940086
## iter 240 value 239.333908
## iter 250 value 233.388802
## iter 260 value 227.590944
## iter 270 value 222.207832
## iter 280 value 215.747398
## iter 290 value 213.112564
## iter 300 value 211.772907
## iter 310 value 209.216559
## iter 320 value 206.560042
## iter 330 value 201.043360
## iter 340 value 195.686972
## iter 350 value 193.049015
## iter 360 value 191.195361
## iter 370 value 189.194753
## iter 380 value 185.021034
## iter 390 value 179.175786
## iter 400 value 174.751843
## iter 410 value 168.371510
## iter 420 value 163.089337
## iter 430 value 160.641851
## iter 440 value 157.782823
## iter 450 value 155.574119
## iter 460 value 153.662207
## iter 470 value 152.531811
## iter 480 value 152.035849
## iter 490 value 151.848877
## iter 500 value 151.620446
## final  value 151.620446 
## stopped after 500 iterations
## # weights:  15
## initial  value 1381310.525640 
## iter  10 value 184370.825597
## iter  20 value 15721.272210
## iter  30 value 11172.698691
## iter  40 value 11001.786749
## final  value 11001.231031 
## converged
## # weights:  36
## initial  value 1410413.643841 
## iter  10 value 32670.958696
## iter  20 value 5985.176422
## iter  30 value 4062.968108
## iter  40 value 3603.317766
## iter  50 value 2455.779456
## iter  60 value 2038.166763
## iter  70 value 2015.887271
## iter  80 value 2013.609241
## iter  90 value 2010.928127
## iter 100 value 1916.382925
## iter 110 value 1911.572087
## iter 120 value 1907.030525
## iter 130 value 1720.279669
## iter 140 value 1540.677944
## iter 150 value 1462.763899
## iter 160 value 1437.279325
## iter 170 value 1420.936912
## iter 180 value 1408.317281
## iter 190 value 1401.429380
## iter 200 value 1400.333752
## iter 210 value 1400.027423
## iter 220 value 1399.760565
## iter 230 value 1398.276603
## iter 240 value 1396.323755
## iter 250 value 1394.802856
## iter 260 value 1385.170117
## iter 270 value 1335.168268
## iter 280 value 1310.131828
## iter 290 value 1291.711755
## iter 300 value 1218.464369
## iter 310 value 1160.446544
## iter 320 value 1131.307474
## iter 330 value 1121.482658
## iter 340 value 1118.833424
## iter 350 value 1117.327987
## iter 360 value 1116.995894
## iter 370 value 1115.660457
## iter 380 value 1113.325088
## iter 390 value 1111.638535
## iter 400 value 1111.069706
## iter 410 value 1110.179662
## iter 420 value 1109.640260
## iter 430 value 1109.603825
## iter 440 value 1108.612187
## iter 450 value 1106.903230
## iter 460 value 1099.437427
## iter 470 value 1099.145668
## iter 480 value 1099.069776
## iter 490 value 1099.052372
## iter 500 value 1098.988013
## final  value 1098.988013 
## stopped after 500 iterations
## # weights:  71
## initial  value 1394447.666814 
## iter  10 value 2826.643814
## iter  20 value 1833.045391
## iter  30 value 1441.059567
## iter  40 value 1186.198686
## iter  50 value 1122.980105
## iter  60 value 990.774449
## iter  70 value 917.126152
## iter  80 value 888.147744
## iter  90 value 869.283608
## iter 100 value 861.642637
## iter 110 value 855.402442
## iter 120 value 846.343812
## iter 130 value 838.821408
## iter 140 value 832.590553
## iter 150 value 829.996882
## iter 160 value 826.397287
## iter 170 value 815.903651
## iter 180 value 800.265154
## iter 190 value 763.671146
## iter 200 value 717.514047
## iter 210 value 697.715782
## iter 220 value 685.035693
## iter 230 value 680.477151
## iter 240 value 674.213914
## iter 250 value 671.798670
## iter 260 value 670.694350
## iter 270 value 668.844109
## iter 280 value 667.723651
## iter 290 value 666.628193
## iter 300 value 666.557366
## iter 310 value 666.418414
## iter 320 value 666.181009
## iter 330 value 666.068050
## iter 340 value 665.818294
## iter 350 value 665.684216
## iter 360 value 665.680074
## iter 370 value 665.334277
## iter 380 value 664.646170
## iter 390 value 664.348638
## iter 400 value 664.268534
## iter 410 value 664.243968
## iter 420 value 664.225808
## iter 430 value 664.137960
## iter 440 value 664.025734
## iter 450 value 663.843875
## iter 460 value 663.784689
## iter 470 value 663.719206
## iter 480 value 663.635248
## iter 490 value 663.575065
## iter 500 value 663.560773
## final  value 663.560773 
## stopped after 500 iterations
## # weights:  106
## initial  value 1377845.062161 
## iter  10 value 1451.872650
## iter  20 value 1048.465775
## iter  30 value 941.434261
## iter  40 value 833.039762
## iter  50 value 790.930781
## iter  60 value 732.391620
## iter  70 value 671.843922
## iter  80 value 613.133013
## iter  90 value 576.317084
## iter 100 value 537.065821
## iter 110 value 509.144136
## iter 120 value 489.510638
## iter 130 value 476.500474
## iter 140 value 461.585939
## iter 150 value 446.853469
## iter 160 value 431.996101
## iter 170 value 416.188914
## iter 180 value 404.786707
## iter 190 value 395.269877
## iter 200 value 387.668751
## iter 210 value 383.736202
## iter 220 value 382.126953
## iter 230 value 381.132380
## iter 240 value 378.451786
## iter 250 value 374.045130
## iter 260 value 370.042030
## iter 270 value 366.786100
## iter 280 value 362.388748
## iter 290 value 355.554972
## iter 300 value 351.229886
## iter 310 value 343.293849
## iter 320 value 337.288438
## iter 330 value 330.580207
## iter 340 value 319.617001
## iter 350 value 309.011403
## iter 360 value 302.039542
## iter 370 value 293.588705
## iter 380 value 287.556545
## iter 390 value 283.258416
## iter 400 value 282.079198
## iter 410 value 281.582408
## iter 420 value 279.368843
## iter 430 value 277.348277
## iter 440 value 277.237064
## iter 450 value 276.994981
## iter 460 value 276.367440
## iter 470 value 275.532852
## iter 480 value 274.567877
## iter 490 value 273.641980
## iter 500 value 273.332181
## final  value 273.332181 
## stopped after 500 iterations
## # weights:  141
## initial  value 1409418.855055 
## iter  10 value 1497.388267
## iter  20 value 1072.109825
## iter  30 value 885.309188
## iter  40 value 798.911587
## iter  50 value 741.490090
## iter  60 value 689.951782
## iter  70 value 607.433960
## iter  80 value 561.055202
## iter  90 value 505.002556
## iter 100 value 476.023076
## iter 110 value 439.591960
## iter 120 value 417.373204
## iter 130 value 404.256262
## iter 140 value 391.358798
## iter 150 value 373.322501
## iter 160 value 362.422480
## iter 170 value 356.510595
## iter 180 value 350.837281
## iter 190 value 347.196708
## iter 200 value 340.311211
## iter 210 value 323.330128
## iter 220 value 309.196291
## iter 230 value 295.520065
## iter 240 value 287.155113
## iter 250 value 282.687722
## iter 260 value 277.071364
## iter 270 value 267.122993
## iter 280 value 254.951753
## iter 290 value 250.295892
## iter 300 value 248.399695
## iter 310 value 245.078835
## iter 320 value 241.816856
## iter 330 value 239.177883
## iter 340 value 237.054524
## iter 350 value 234.528651
## iter 360 value 230.029685
## iter 370 value 226.219806
## iter 380 value 218.989053
## iter 390 value 208.629102
## iter 400 value 201.731775
## iter 410 value 196.280224
## iter 420 value 191.298374
## iter 430 value 184.559472
## iter 440 value 177.169356
## iter 450 value 172.989802
## iter 460 value 171.461455
## iter 470 value 168.946556
## iter 480 value 167.724256
## iter 490 value 166.510564
## iter 500 value 165.788840
## final  value 165.788840 
## stopped after 500 iterations
## # weights:  15
## initial  value 1389940.740895 
## iter  10 value 9957.104566
## iter  20 value 6811.969095
## iter  30 value 4157.058720
## iter  40 value 3859.551176
## iter  50 value 3506.945601
## iter  60 value 2518.548077
## iter  70 value 2230.526366
## iter  80 value 2166.642866
## iter  90 value 2125.440613
## iter 100 value 1890.545496
## iter 110 value 1750.353042
## iter 120 value 1706.437328
## iter 130 value 1678.778925
## iter 140 value 1540.241014
## iter 150 value 1503.841040
## iter 160 value 1493.883499
## iter 170 value 1479.923571
## iter 180 value 1479.266851
## iter 190 value 1479.260791
## final  value 1479.259761 
## converged
## # weights:  36
## initial  value 1377192.040010 
## iter  10 value 8206.928004
## iter  20 value 4747.656249
## iter  30 value 3849.258071
## iter  40 value 3335.763157
## iter  50 value 2781.027802
## iter  60 value 2148.715607
## iter  70 value 1643.980617
## iter  80 value 1601.475291
## iter  90 value 1448.126870
## iter 100 value 1315.531294
## iter 110 value 1268.228531
## iter 120 value 1247.851899
## iter 130 value 1220.169504
## iter 140 value 1211.048350
## iter 150 value 1202.802349
## iter 160 value 1200.585349
## iter 170 value 1194.113065
## iter 180 value 1191.520538
## iter 190 value 1191.446954
## iter 190 value 1191.446948
## iter 190 value 1191.446948
## final  value 1191.446948 
## converged
## # weights:  71
## initial  value 1421186.199171 
## iter  10 value 2270.143658
## iter  20 value 1287.836057
## iter  30 value 1117.254077
## iter  40 value 1061.520496
## iter  50 value 1011.991890
## iter  60 value 985.568951
## iter  70 value 965.506736
## iter  80 value 936.931759
## iter  90 value 921.505914
## iter 100 value 915.358551
## iter 110 value 908.873493
## iter 120 value 899.396854
## iter 130 value 890.312828
## iter 140 value 885.065368
## iter 150 value 880.378233
## iter 160 value 877.696333
## iter 170 value 872.260532
## iter 180 value 868.675828
## iter 190 value 866.533102
## iter 200 value 865.245994
## iter 210 value 862.099783
## iter 220 value 852.971274
## iter 230 value 842.956800
## iter 240 value 841.777857
## iter 250 value 841.682616
## iter 260 value 841.039158
## iter 270 value 838.514937
## iter 280 value 837.744798
## iter 290 value 837.572287
## iter 300 value 837.569315
## iter 310 value 837.567939
## iter 320 value 837.566484
## iter 330 value 837.565088
## iter 340 value 837.564339
## iter 350 value 837.563990
## final  value 837.563968 
## converged
## # weights:  106
## initial  value 1458357.223224 
## iter  10 value 2650.438708
## iter  20 value 1390.316058
## iter  30 value 1179.426958
## iter  40 value 1078.408409
## iter  50 value 969.755398
## iter  60 value 908.456597
## iter  70 value 856.850537
## iter  80 value 831.129298
## iter  90 value 806.745293
## iter 100 value 788.407594
## iter 110 value 765.438546
## iter 120 value 731.292665
## iter 130 value 714.241555
## iter 140 value 704.463430
## iter 150 value 699.519538
## iter 160 value 697.142361
## iter 170 value 695.026594
## iter 180 value 693.583258
## iter 190 value 692.698833
## iter 200 value 691.377784
## iter 210 value 690.068715
## iter 220 value 689.563755
## iter 230 value 688.624554
## iter 240 value 687.347886
## iter 250 value 686.883236
## iter 260 value 686.629247
## iter 270 value 686.160763
## iter 280 value 682.914465
## iter 290 value 677.866397
## iter 300 value 673.668326
## iter 310 value 669.345711
## iter 320 value 666.599214
## iter 330 value 665.535321
## iter 340 value 665.032655
## iter 350 value 664.895299
## iter 360 value 664.889353
## iter 370 value 664.888947
## final  value 664.888734 
## converged
## # weights:  141
## initial  value 1482179.119857 
## iter  10 value 1589.200992
## iter  20 value 1209.800561
## iter  30 value 1025.775127
## iter  40 value 932.003206
## iter  50 value 851.903898
## iter  60 value 778.138444
## iter  70 value 743.885754
## iter  80 value 706.370840
## iter  90 value 683.082198
## iter 100 value 662.672223
## iter 110 value 648.697112
## iter 120 value 640.045876
## iter 130 value 631.821129
## iter 140 value 623.775969
## iter 150 value 616.631496
## iter 160 value 612.598898
## iter 170 value 609.317352
## iter 180 value 604.708204
## iter 190 value 599.811574
## iter 200 value 593.373256
## iter 210 value 582.025036
## iter 220 value 575.090261
## iter 230 value 570.716678
## iter 240 value 565.288739
## iter 250 value 556.320603
## iter 260 value 550.199671
## iter 270 value 545.737524
## iter 280 value 542.086459
## iter 290 value 540.826504
## iter 300 value 539.078972
## iter 310 value 536.705876
## iter 320 value 535.415073
## iter 330 value 534.843660
## iter 340 value 534.329765
## iter 350 value 533.836938
## iter 360 value 532.414704
## iter 370 value 528.686253
## iter 380 value 526.045872
## iter 390 value 525.100521
## iter 400 value 523.919094
## iter 410 value 522.178684
## iter 420 value 521.334508
## iter 430 value 520.923906
## iter 440 value 520.686507
## iter 450 value 520.445265
## iter 460 value 520.287517
## iter 470 value 520.135982
## iter 480 value 519.623294
## iter 490 value 518.316619
## iter 500 value 517.477285
## final  value 517.477285 
## stopped after 500 iterations
## # weights:  15
## initial  value 1460601.304015 
## iter  10 value 10845.612278
## iter  20 value 5993.198342
## iter  30 value 3433.962091
## iter  40 value 2928.356814
## iter  50 value 1820.880171
## iter  60 value 1357.694256
## iter  70 value 1300.219164
## iter  80 value 1240.196139
## iter  90 value 1222.548569
## iter 100 value 1216.767215
## iter 110 value 1212.267401
## iter 120 value 1205.911100
## iter 130 value 1203.921042
## iter 140 value 1203.405736
## iter 150 value 1202.597981
## final  value 1202.588125 
## converged
## # weights:  36
## initial  value 1386049.109483 
## iter  10 value 5560.070422
## iter  20 value 3308.235498
## iter  30 value 2723.605917
## iter  40 value 2390.973078
## iter  50 value 2053.048943
## iter  60 value 1838.203934
## iter  70 value 1560.640776
## iter  80 value 1382.781268
## iter  90 value 1241.549306
## iter 100 value 1209.984476
## iter 110 value 1189.113345
## iter 120 value 1171.062287
## iter 130 value 1162.547225
## iter 140 value 1135.473078
## iter 150 value 1061.637193
## iter 160 value 1037.205400
## iter 170 value 980.467976
## iter 180 value 945.452252
## iter 190 value 936.328078
## iter 200 value 936.240271
## iter 210 value 936.092762
## iter 220 value 935.510123
## iter 230 value 934.805458
## iter 240 value 934.766385
## iter 250 value 934.762276
## iter 250 value 934.762267
## iter 250 value 934.762267
## final  value 934.762267 
## converged
## # weights:  71
## initial  value 1423258.262944 
## iter  10 value 2920.279031
## iter  20 value 1496.410974
## iter  30 value 1068.457708
## iter  40 value 965.241150
## iter  50 value 916.486679
## iter  60 value 871.761186
## iter  70 value 839.288220
## iter  80 value 818.364130
## iter  90 value 806.435723
## iter 100 value 792.996491
## iter 110 value 779.629277
## iter 120 value 772.532256
## iter 130 value 763.287808
## iter 140 value 754.114248
## iter 150 value 750.249048
## iter 160 value 741.709307
## iter 170 value 727.827930
## iter 180 value 714.500792
## iter 190 value 706.468660
## iter 200 value 696.559419
## iter 210 value 683.287811
## iter 220 value 671.861897
## iter 230 value 666.893207
## iter 240 value 664.085524
## iter 250 value 663.631153
## iter 260 value 663.366670
## iter 270 value 663.123084
## iter 280 value 662.988047
## iter 290 value 662.844573
## iter 300 value 662.825733
## iter 310 value 662.781841
## iter 320 value 662.741437
## iter 330 value 662.552569
## iter 340 value 662.234814
## iter 350 value 661.915456
## iter 360 value 661.731221
## iter 370 value 661.539484
## iter 380 value 661.484411
## iter 390 value 661.443712
## iter 400 value 661.419390
## iter 410 value 660.847146
## iter 420 value 660.422768
## iter 430 value 660.224488
## iter 440 value 660.207339
## iter 450 value 660.152524
## iter 460 value 659.955246
## iter 470 value 659.095394
## iter 480 value 658.711023
## iter 490 value 658.606856
## iter 500 value 658.477546
## final  value 658.477546 
## stopped after 500 iterations
## # weights:  106
## initial  value 1347528.453424 
## iter  10 value 1382.944585
## iter  20 value 1145.173218
## iter  30 value 955.624448
## iter  40 value 887.371954
## iter  50 value 822.553812
## iter  60 value 733.526786
## iter  70 value 685.481743
## iter  80 value 627.381122
## iter  90 value 588.142681
## iter 100 value 550.496479
## iter 110 value 524.180413
## iter 120 value 516.558335
## iter 130 value 506.573196
## iter 140 value 496.584657
## iter 150 value 480.199127
## iter 160 value 469.275325
## iter 170 value 459.163096
## iter 180 value 450.583451
## iter 190 value 436.234000
## iter 200 value 429.628273
## iter 210 value 426.342400
## iter 220 value 423.912178
## iter 230 value 420.848633
## iter 240 value 415.754128
## iter 250 value 404.584755
## iter 260 value 395.758275
## iter 270 value 390.693751
## iter 280 value 384.542451
## iter 290 value 379.733840
## iter 300 value 375.824221
## iter 310 value 373.130574
## iter 320 value 371.511155
## iter 330 value 370.682927
## iter 340 value 370.272343
## iter 350 value 370.227577
## iter 360 value 370.217587
## iter 370 value 370.213776
## iter 380 value 370.212131
## iter 390 value 370.211417
## iter 400 value 370.211117
## final  value 370.211080 
## converged
## # weights:  141
## initial  value 1447720.435724 
## iter  10 value 1380.455883
## iter  20 value 1096.680256
## iter  30 value 943.796371
## iter  40 value 819.854950
## iter  50 value 730.111775
## iter  60 value 624.753192
## iter  70 value 561.658234
## iter  80 value 500.622888
## iter  90 value 449.636978
## iter 100 value 408.997137
## iter 110 value 375.047197
## iter 120 value 355.358987
## iter 130 value 340.769838
## iter 140 value 324.842520
## iter 150 value 304.097478
## iter 160 value 293.421824
## iter 170 value 284.906374
## iter 180 value 280.754635
## iter 190 value 276.633769
## iter 200 value 272.420532
## iter 210 value 268.583599
## iter 220 value 265.071700
## iter 230 value 262.727775
## iter 240 value 260.709253
## iter 250 value 256.814446
## iter 260 value 251.884876
## iter 270 value 248.764738
## iter 280 value 246.287595
## iter 290 value 245.295705
## iter 300 value 244.629137
## iter 310 value 243.319949
## iter 320 value 242.327345
## iter 330 value 240.725145
## iter 340 value 237.717675
## iter 350 value 235.261471
## iter 360 value 231.683225
## iter 370 value 228.473136
## iter 380 value 226.201439
## iter 390 value 224.920342
## iter 400 value 223.886913
## iter 410 value 223.067058
## iter 420 value 222.064956
## iter 430 value 221.605381
## iter 440 value 220.120005
## iter 450 value 214.172930
## iter 460 value 212.879307
## iter 470 value 212.233874
## iter 480 value 211.984394
## iter 490 value 211.842031
## iter 500 value 211.770536
## final  value 211.770536 
## stopped after 500 iterations
## # weights:  15
## initial  value 1395117.114379 
## iter  10 value 5573.911464
## iter  20 value 5373.943474
## iter  30 value 5082.060489
## iter  40 value 4693.537585
## iter  50 value 4246.704092
## iter  60 value 3482.209754
## iter  70 value 1806.018618
## iter  80 value 1596.093289
## iter  90 value 1280.091187
## iter 100 value 1231.069426
## iter 110 value 1230.468305
## iter 120 value 1216.221651
## iter 130 value 1205.715527
## iter 140 value 1205.344869
## iter 150 value 1202.787255
## iter 160 value 1199.790329
## iter 170 value 1199.549015
## iter 180 value 1198.932665
## iter 190 value 1198.013387
## iter 200 value 1197.931163
## iter 210 value 1197.892756
## iter 220 value 1197.756518
## final  value 1197.754722 
## converged
## # weights:  36
## initial  value 1379593.852359 
## iter  10 value 6612.326334
## iter  20 value 3379.828877
## iter  30 value 2627.730222
## iter  40 value 2054.781541
## iter  50 value 1751.934847
## iter  60 value 1614.494965
## iter  70 value 1550.486507
## iter  80 value 1499.603219
## iter  90 value 1450.359980
## iter 100 value 1400.779627
## iter 110 value 1237.642239
## iter 120 value 1166.209526
## iter 130 value 1145.249601
## iter 140 value 1143.104619
## iter 150 value 1142.209020
## iter 160 value 1141.708577
## iter 170 value 1141.573814
## iter 180 value 1141.452003
## iter 190 value 1141.244262
## iter 200 value 1140.966160
## iter 210 value 1140.888086
## iter 220 value 1140.797805
## iter 230 value 1140.739534
## final  value 1140.725278 
## converged
## # weights:  71
## initial  value 1408035.109725 
## iter  10 value 5552.077174
## iter  20 value 2361.826117
## iter  30 value 1581.244059
## iter  40 value 1310.866001
## iter  50 value 1068.153027
## iter  60 value 993.523878
## iter  70 value 924.454721
## iter  80 value 903.824762
## iter  90 value 858.890838
## iter 100 value 808.871928
## iter 110 value 787.261738
## iter 120 value 772.834143
## iter 130 value 769.749350
## iter 140 value 767.665152
## iter 150 value 767.174416
## iter 160 value 767.105481
## iter 170 value 766.938027
## iter 180 value 766.691497
## iter 190 value 765.138805
## iter 200 value 763.344142
## iter 210 value 760.543632
## iter 220 value 744.887509
## iter 230 value 718.439613
## iter 240 value 672.402077
## iter 250 value 656.493841
## iter 260 value 653.648861
## iter 270 value 653.042136
## iter 280 value 651.476944
## iter 290 value 649.949663
## iter 300 value 645.932168
## iter 310 value 640.642831
## iter 320 value 630.178241
## iter 330 value 617.020363
## iter 340 value 604.642938
## iter 350 value 599.532930
## iter 360 value 597.520000
## iter 370 value 596.578439
## iter 380 value 594.545620
## iter 390 value 593.481215
## iter 400 value 592.845240
## iter 410 value 592.726385
## iter 420 value 592.595018
## iter 430 value 592.119317
## iter 440 value 587.604893
## iter 450 value 585.347784
## iter 460 value 573.218551
## iter 470 value 566.069619
## iter 480 value 559.791449
## iter 490 value 556.161288
## iter 500 value 555.700304
## final  value 555.700304 
## stopped after 500 iterations
## # weights:  106
## initial  value 1481931.662579 
## iter  10 value 1788.057403
## iter  20 value 1144.757178
## iter  30 value 947.477339
## iter  40 value 826.454253
## iter  50 value 753.277889
## iter  60 value 689.485542
## iter  70 value 632.086125
## iter  80 value 589.056226
## iter  90 value 555.001617
## iter 100 value 527.257388
## iter 110 value 508.216162
## iter 120 value 485.008446
## iter 130 value 460.735692
## iter 140 value 449.900788
## iter 150 value 438.661894
## iter 160 value 427.993808
## iter 170 value 420.415600
## iter 180 value 409.580102
## iter 190 value 395.557814
## iter 200 value 389.355945
## iter 210 value 384.194798
## iter 220 value 380.985031
## iter 230 value 376.819261
## iter 240 value 371.607751
## iter 250 value 368.001113
## iter 260 value 362.403183
## iter 270 value 355.963157
## iter 280 value 347.533128
## iter 290 value 339.272426
## iter 300 value 329.717481
## iter 310 value 325.812621
## iter 320 value 323.397915
## iter 330 value 320.106031
## iter 340 value 317.850935
## iter 350 value 317.012738
## iter 360 value 316.583239
## iter 370 value 316.011412
## iter 380 value 315.525706
## iter 390 value 313.647982
## iter 400 value 311.487533
## iter 410 value 310.246526
## iter 420 value 309.118465
## iter 430 value 308.544993
## iter 440 value 308.489135
## iter 450 value 308.432721
## iter 460 value 308.382531
## iter 470 value 308.347906
## iter 480 value 308.281954
## iter 490 value 308.123232
## iter 500 value 307.536282
## final  value 307.536282 
## stopped after 500 iterations
## # weights:  141
## initial  value 1412261.758369 
## iter  10 value 1688.400932
## iter  20 value 1153.671944
## iter  30 value 975.153052
## iter  40 value 840.663132
## iter  50 value 677.691954
## iter  60 value 604.014355
## iter  70 value 533.126764
## iter  80 value 452.181835
## iter  90 value 431.568611
## iter 100 value 410.775856
## iter 110 value 391.430098
## iter 120 value 375.066860
## iter 130 value 361.954439
## iter 140 value 351.615831
## iter 150 value 338.250356
## iter 160 value 325.224783
## iter 170 value 309.828655
## iter 180 value 291.820143
## iter 190 value 269.817536
## iter 200 value 257.849241
## iter 210 value 246.889675
## iter 220 value 236.868028
## iter 230 value 231.522319
## iter 240 value 227.365233
## iter 250 value 222.008124
## iter 260 value 216.929525
## iter 270 value 212.696346
## iter 280 value 209.348707
## iter 290 value 207.922812
## iter 300 value 207.024516
## iter 310 value 205.501769
## iter 320 value 204.234290
## iter 330 value 202.447109
## iter 340 value 200.343652
## iter 350 value 197.830967
## iter 360 value 194.332959
## iter 370 value 191.326479
## iter 380 value 187.322453
## iter 390 value 183.707182
## iter 400 value 179.843003
## iter 410 value 177.723711
## iter 420 value 174.476939
## iter 430 value 172.104428
## iter 440 value 170.379525
## iter 450 value 168.322601
## iter 460 value 167.024371
## iter 470 value 165.682621
## iter 480 value 165.400846
## iter 490 value 164.938005
## iter 500 value 163.858923
## final  value 163.858923 
## stopped after 500 iterations
## # weights:  15
## initial  value 1408337.019516 
## iter  10 value 6824.951014
## iter  20 value 2344.949372
## iter  30 value 1616.638683
## iter  40 value 1599.129596
## iter  50 value 1539.351606
## iter  60 value 1509.819385
## iter  70 value 1504.455454
## iter  80 value 1498.605015
## iter  90 value 1484.554769
## iter 100 value 1480.187549
## iter 110 value 1477.959313
## iter 120 value 1472.202352
## iter 130 value 1471.664487
## iter 140 value 1471.418285
## iter 150 value 1469.860182
## iter 160 value 1469.129222
## iter 170 value 1468.587170
## iter 180 value 1466.945421
## iter 190 value 1466.622794
## iter 200 value 1466.582863
## iter 210 value 1466.031611
## iter 220 value 1465.579691
## iter 230 value 1465.459074
## iter 240 value 1464.488454
## iter 250 value 1464.221267
## iter 260 value 1464.217676
## final  value 1464.217371 
## converged
## # weights:  36
## initial  value 1425167.244067 
## iter  10 value 22944.644977
## iter  20 value 15437.894358
## iter  30 value 8921.251755
## iter  40 value 5121.262438
## iter  50 value 2495.995549
## iter  60 value 2301.895436
## iter  70 value 2110.763849
## iter  80 value 1883.726124
## iter  90 value 1723.570853
## iter 100 value 1618.978671
## iter 110 value 1482.745147
## iter 120 value 1451.631692
## iter 130 value 1441.221251
## iter 140 value 1428.944816
## iter 150 value 1426.826997
## iter 160 value 1424.245625
## iter 170 value 1424.077828
## iter 180 value 1422.391008
## iter 190 value 1418.410627
## iter 200 value 1416.332461
## iter 210 value 1413.508181
## iter 220 value 1411.720369
## iter 230 value 1410.047818
## final  value 1410.024497 
## converged
## # weights:  71
## initial  value 1402787.659337 
## iter  10 value 2139.335295
## iter  20 value 1233.339988
## iter  30 value 1102.449878
## iter  40 value 1003.989120
## iter  50 value 952.536592
## iter  60 value 907.688488
## iter  70 value 843.327823
## iter  80 value 818.014758
## iter  90 value 790.047569
## iter 100 value 770.554791
## iter 110 value 763.038802
## iter 120 value 757.118456
## iter 130 value 752.237938
## iter 140 value 746.107912
## iter 150 value 744.166768
## iter 160 value 742.454876
## iter 170 value 740.125753
## iter 180 value 736.870311
## iter 190 value 733.288067
## iter 200 value 721.830832
## iter 210 value 699.014664
## iter 220 value 683.134763
## iter 230 value 671.245587
## iter 240 value 658.528954
## iter 250 value 645.079206
## iter 260 value 635.997893
## iter 270 value 631.462283
## iter 280 value 627.255604
## iter 290 value 625.367804
## iter 300 value 624.541489
## iter 310 value 623.155179
## iter 320 value 619.368524
## iter 330 value 615.042497
## iter 340 value 609.505450
## iter 350 value 604.553953
## iter 360 value 601.665413
## iter 370 value 598.974049
## iter 380 value 597.040326
## iter 390 value 596.251802
## iter 400 value 595.469277
## iter 410 value 594.156251
## iter 420 value 589.519005
## iter 430 value 582.757583
## iter 440 value 580.127772
## iter 450 value 578.836864
## iter 460 value 576.068673
## iter 470 value 569.564480
## iter 480 value 565.064446
## iter 490 value 562.773166
## iter 500 value 561.997817
## final  value 561.997817 
## stopped after 500 iterations
## # weights:  106
## initial  value 1423378.459551 
## iter  10 value 1377.074981
## iter  20 value 1097.183045
## iter  30 value 959.382278
## iter  40 value 845.301732
## iter  50 value 754.050693
## iter  60 value 688.431840
## iter  70 value 629.499535
## iter  80 value 559.324227
## iter  90 value 520.294048
## iter 100 value 498.700802
## iter 110 value 471.483228
## iter 120 value 452.152360
## iter 130 value 429.687664
## iter 140 value 410.093096
## iter 150 value 399.368782
## iter 160 value 392.324369
## iter 170 value 388.206879
## iter 180 value 383.876188
## iter 190 value 379.172586
## iter 200 value 376.459270
## iter 210 value 373.542895
## iter 220 value 371.709141
## iter 230 value 370.683866
## iter 240 value 368.439784
## iter 250 value 365.603425
## iter 260 value 360.484459
## iter 270 value 353.188138
## iter 280 value 346.364478
## iter 290 value 334.873859
## iter 300 value 328.001820
## iter 310 value 323.700707
## iter 320 value 317.134865
## iter 330 value 311.445542
## iter 340 value 308.125513
## iter 350 value 306.012470
## iter 360 value 303.351582
## iter 370 value 299.955389
## iter 380 value 294.867113
## iter 390 value 292.039991
## iter 400 value 288.777171
## iter 410 value 285.387811
## iter 420 value 284.543460
## iter 430 value 283.872564
## iter 440 value 283.796577
## iter 450 value 283.740394
## iter 460 value 283.683711
## iter 470 value 283.632376
## iter 480 value 283.563969
## iter 490 value 283.456389
## iter 500 value 283.290178
## final  value 283.290178 
## stopped after 500 iterations
## # weights:  141
## initial  value 1454681.732369 
## iter  10 value 3136.383843
## iter  20 value 1468.363303
## iter  30 value 1147.435707
## iter  40 value 1020.054075
## iter  50 value 918.211394
## iter  60 value 879.660817
## iter  70 value 841.471708
## iter  80 value 773.037328
## iter  90 value 706.920798
## iter 100 value 675.449155
## iter 110 value 658.913148
## iter 120 value 623.836854
## iter 130 value 580.568717
## iter 140 value 555.299135
## iter 150 value 536.754261
## iter 160 value 506.529893
## iter 170 value 475.020203
## iter 180 value 439.714266
## iter 190 value 419.342574
## iter 200 value 408.697825
## iter 210 value 395.369381
## iter 220 value 372.170533
## iter 230 value 363.248617
## iter 240 value 353.452445
## iter 250 value 346.552086
## iter 260 value 336.152194
## iter 270 value 331.085999
## iter 280 value 327.249263
## iter 290 value 325.567630
## iter 300 value 325.130344
## iter 310 value 324.259470
## iter 320 value 322.754446
## iter 330 value 320.357736
## iter 340 value 317.334262
## iter 350 value 312.517305
## iter 360 value 303.374180
## iter 370 value 293.179202
## iter 380 value 287.624872
## iter 390 value 280.916699
## iter 400 value 271.533159
## iter 410 value 267.025220
## iter 420 value 263.675665
## iter 430 value 261.402399
## iter 440 value 259.359348
## iter 450 value 258.196668
## iter 460 value 257.619550
## iter 470 value 256.540648
## iter 480 value 254.214670
## iter 490 value 252.590476
## iter 500 value 251.335811
## final  value 251.335811 
## stopped after 500 iterations
## # weights:  15
## initial  value 1402695.316282 
## iter  10 value 6380.352761
## iter  20 value 6002.326924
## iter  30 value 5980.748924
## iter  40 value 5391.519870
## iter  50 value 2797.764418
## iter  60 value 1899.642602
## iter  70 value 1805.568552
## iter  80 value 1743.800828
## iter  90 value 1733.607811
## iter 100 value 1730.180347
## iter 110 value 1725.973992
## iter 120 value 1723.985045
## iter 130 value 1723.667320
## iter 140 value 1721.284011
## iter 150 value 1718.532893
## iter 160 value 1716.080746
## iter 170 value 1715.355982
## final  value 1715.354998 
## converged
## # weights:  36
## initial  value 1402115.490621 
## iter  10 value 37457.283603
## iter  20 value 4746.373495
## iter  30 value 4508.758656
## iter  40 value 4477.381341
## iter  50 value 4469.324491
## iter  60 value 4469.159192
## iter  70 value 4467.720435
## iter  80 value 4467.398012
## iter  90 value 4466.614499
## iter 100 value 4463.567578
## iter 110 value 4459.324481
## iter 120 value 4441.872973
## iter 130 value 4391.902141
## iter 140 value 4269.244472
## iter 150 value 3806.950846
## iter 160 value 2855.690192
## iter 170 value 1995.006446
## iter 180 value 1830.326712
## iter 190 value 1766.109259
## iter 200 value 1735.490826
## iter 210 value 1720.479163
## iter 220 value 1691.708398
## iter 230 value 1688.442968
## iter 240 value 1684.053231
## iter 250 value 1660.984423
## iter 260 value 1637.962624
## iter 270 value 1633.619952
## iter 280 value 1621.569095
## iter 290 value 1601.876626
## iter 300 value 1563.497877
## iter 310 value 1544.059958
## iter 320 value 1540.367313
## iter 330 value 1537.667897
## iter 340 value 1536.758936
## iter 350 value 1531.208435
## iter 360 value 1527.663359
## iter 370 value 1507.376364
## iter 380 value 1506.485420
## iter 390 value 1505.832306
## iter 400 value 1505.045965
## iter 410 value 1504.275794
## iter 420 value 1503.730055
## iter 430 value 1503.236974
## iter 440 value 1502.737656
## iter 450 value 1502.712578
## iter 450 value 1502.712574
## final  value 1502.712092 
## converged
## # weights:  71
## initial  value 1393289.411761 
## iter  10 value 4715.848397
## iter  20 value 2106.483288
## iter  30 value 1625.924165
## iter  40 value 1352.253325
## iter  50 value 1080.157973
## iter  60 value 947.041363
## iter  70 value 791.249133
## iter  80 value 712.079736
## iter  90 value 678.361034
## iter 100 value 664.725343
## iter 110 value 656.667570
## iter 120 value 649.704592
## iter 130 value 645.387812
## iter 140 value 636.312946
## iter 150 value 632.996933
## iter 160 value 631.598238
## iter 170 value 627.059316
## iter 180 value 620.045257
## iter 190 value 613.328928
## iter 200 value 593.534422
## iter 210 value 581.817028
## iter 220 value 571.205139
## iter 230 value 564.966814
## iter 240 value 562.556104
## iter 250 value 560.892197
## iter 260 value 558.994814
## iter 270 value 556.731284
## iter 280 value 555.208620
## iter 290 value 554.899883
## iter 300 value 554.884008
## iter 310 value 554.866694
## iter 320 value 554.797957
## iter 330 value 554.728978
## iter 340 value 554.710732
## iter 350 value 554.638751
## iter 360 value 554.445114
## iter 370 value 554.368936
## iter 380 value 554.273082
## iter 390 value 554.203010
## iter 400 value 554.140211
## iter 410 value 554.117987
## iter 420 value 554.065932
## iter 430 value 554.040054
## iter 440 value 554.038469
## iter 450 value 554.036801
## iter 460 value 554.034939
## iter 470 value 554.020212
## iter 480 value 554.004281
## iter 490 value 554.000690
## iter 500 value 553.991954
## final  value 553.991954 
## stopped after 500 iterations
## # weights:  106
## initial  value 1403306.937650 
## iter  10 value 1489.807939
## iter  20 value 1118.596824
## iter  30 value 916.295527
## iter  40 value 826.286299
## iter  50 value 756.741621
## iter  60 value 719.360531
## iter  70 value 673.942788
## iter  80 value 639.978483
## iter  90 value 600.701460
## iter 100 value 564.639069
## iter 110 value 540.355896
## iter 120 value 523.115761
## iter 130 value 501.586409
## iter 140 value 490.563376
## iter 150 value 480.253887
## iter 160 value 455.425952
## iter 170 value 435.455089
## iter 180 value 414.290597
## iter 190 value 390.477930
## iter 200 value 373.551827
## iter 210 value 361.995593
## iter 220 value 356.848363
## iter 230 value 354.638246
## iter 240 value 351.518882
## iter 250 value 346.375828
## iter 260 value 341.692049
## iter 270 value 336.120931
## iter 280 value 332.173052
## iter 290 value 327.864140
## iter 300 value 322.130651
## iter 310 value 317.172009
## iter 320 value 313.556966
## iter 330 value 302.769680
## iter 340 value 295.663151
## iter 350 value 290.818510
## iter 360 value 288.719066
## iter 370 value 286.828947
## iter 380 value 285.030791
## iter 390 value 283.897375
## iter 400 value 282.666274
## iter 410 value 282.038230
## iter 420 value 281.638377
## iter 430 value 281.094259
## iter 440 value 281.042650
## iter 450 value 280.966822
## iter 460 value 280.734646
## iter 470 value 280.122408
## iter 480 value 279.632591
## iter 490 value 279.255727
## iter 500 value 279.135127
## final  value 279.135127 
## stopped after 500 iterations
## # weights:  141
## initial  value 1393306.512503 
## iter  10 value 1617.631497
## iter  20 value 1051.249889
## iter  30 value 882.402373
## iter  40 value 761.195814
## iter  50 value 658.833216
## iter  60 value 587.677486
## iter  70 value 546.008241
## iter  80 value 497.987167
## iter  90 value 459.704980
## iter 100 value 429.703528
## iter 110 value 390.436897
## iter 120 value 362.546069
## iter 130 value 342.328978
## iter 140 value 321.231684
## iter 150 value 295.873518
## iter 160 value 272.439508
## iter 170 value 247.664324
## iter 180 value 237.300497
## iter 190 value 228.925650
## iter 200 value 223.642609
## iter 210 value 218.593996
## iter 220 value 213.977332
## iter 230 value 210.851311
## iter 240 value 206.941389
## iter 250 value 203.539263
## iter 260 value 201.824722
## iter 270 value 199.461087
## iter 280 value 197.808906
## iter 290 value 196.920781
## iter 300 value 196.555254
## iter 310 value 195.844090
## iter 320 value 194.231968
## iter 330 value 193.128309
## iter 340 value 191.420532
## iter 350 value 188.587819
## iter 360 value 184.520386
## iter 370 value 179.776055
## iter 380 value 175.859136
## iter 390 value 171.486382
## iter 400 value 166.632662
## iter 410 value 163.699258
## iter 420 value 161.636128
## iter 430 value 157.573503
## iter 440 value 153.444373
## iter 450 value 149.425640
## iter 460 value 146.048195
## iter 470 value 143.776179
## iter 480 value 142.624020
## iter 490 value 141.905486
## iter 500 value 141.439992
## final  value 141.439992 
## stopped after 500 iterations
## # weights:  15
## initial  value 1412123.848788 
## iter  10 value 17816.127669
## iter  20 value 4213.296761
## iter  30 value 3129.584344
## iter  40 value 2501.798233
## iter  50 value 2195.138494
## iter  60 value 1954.790678
## iter  70 value 1732.478504
## iter  80 value 1693.816072
## iter  90 value 1633.072117
## iter 100 value 1614.261929
## iter 110 value 1612.677947
## iter 120 value 1612.338431
## final  value 1612.338009 
## converged
## # weights:  36
## initial  value 1431181.779951 
## iter  10 value 9705.906100
## iter  20 value 7564.749401
## iter  30 value 6574.719690
## iter  40 value 3897.015955
## iter  50 value 2875.560804
## iter  60 value 2099.440875
## iter  70 value 1552.504797
## iter  80 value 1386.601634
## iter  90 value 1340.352030
## iter 100 value 1312.223614
## iter 110 value 1303.925527
## iter 120 value 1299.296974
## iter 130 value 1295.033185
## iter 140 value 1289.054234
## iter 150 value 1271.629045
## iter 160 value 1241.356303
## iter 170 value 1188.991495
## iter 180 value 1162.878653
## iter 190 value 1158.003381
## iter 200 value 1157.145021
## iter 210 value 1156.248857
## iter 220 value 1155.900267
## final  value 1155.889083 
## converged
## # weights:  71
## initial  value 1403758.018815 
## iter  10 value 2486.954813
## iter  20 value 1678.962782
## iter  30 value 1483.676972
## iter  40 value 1403.205838
## iter  50 value 1353.589959
## iter  60 value 1312.588254
## iter  70 value 1284.904676
## iter  80 value 1261.197500
## iter  90 value 1227.126801
## iter 100 value 1170.160491
## iter 110 value 1130.443836
## iter 120 value 1099.530558
## iter 130 value 1078.209711
## iter 140 value 1052.069029
## iter 150 value 1024.489084
## iter 160 value 1014.269621
## iter 170 value 997.177742
## iter 180 value 974.741669
## iter 190 value 951.376296
## iter 200 value 937.397438
## iter 210 value 927.014057
## iter 220 value 920.805077
## iter 230 value 911.812329
## iter 240 value 904.520730
## iter 250 value 901.543821
## iter 260 value 899.694674
## iter 270 value 898.236304
## iter 280 value 897.681775
## iter 290 value 897.239717
## iter 300 value 897.186828
## iter 310 value 897.098651
## iter 320 value 897.038988
## iter 330 value 896.982875
## iter 340 value 896.955409
## iter 350 value 896.944911
## final  value 896.944785 
## converged
## # weights:  106
## initial  value 1382041.950975 
## iter  10 value 1384.000628
## iter  20 value 1101.128149
## iter  30 value 987.671665
## iter  40 value 912.754629
## iter  50 value 858.698285
## iter  60 value 833.833243
## iter  70 value 799.096292
## iter  80 value 761.899846
## iter  90 value 736.607653
## iter 100 value 716.283311
## iter 110 value 701.648636
## iter 120 value 690.761524
## iter 130 value 676.008617
## iter 140 value 666.765224
## iter 150 value 661.799875
## iter 160 value 655.446013
## iter 170 value 650.198377
## iter 180 value 644.866209
## iter 190 value 638.226034
## iter 200 value 632.019667
## iter 210 value 628.095028
## iter 220 value 626.836556
## iter 230 value 625.387357
## iter 240 value 623.240263
## iter 250 value 621.699562
## iter 260 value 620.555860
## iter 270 value 619.360963
## iter 280 value 618.021215
## iter 290 value 617.169069
## iter 300 value 616.029885
## iter 310 value 614.909341
## iter 320 value 613.597784
## iter 330 value 611.404882
## iter 340 value 610.353821
## iter 350 value 609.832772
## iter 360 value 609.447249
## iter 370 value 609.308032
## iter 380 value 609.237639
## iter 390 value 609.180906
## iter 400 value 609.158370
## iter 410 value 609.139588
## iter 420 value 609.138905
## final  value 609.138891 
## converged
## # weights:  141
## initial  value 1385092.616293 
## iter  10 value 1948.394252
## iter  20 value 1232.669851
## iter  30 value 1102.571705
## iter  40 value 982.767266
## iter  50 value 875.009156
## iter  60 value 810.373744
## iter  70 value 777.303965
## iter  80 value 747.839620
## iter  90 value 726.065221
## iter 100 value 707.276226
## iter 110 value 695.120362
## iter 120 value 679.130091
## iter 130 value 659.575796
## iter 140 value 644.640796
## iter 150 value 637.986588
## iter 160 value 632.960017
## iter 170 value 628.043587
## iter 180 value 619.653308
## iter 190 value 615.223589
## iter 200 value 612.360386
## iter 210 value 610.238602
## iter 220 value 608.340191
## iter 230 value 605.428699
## iter 240 value 602.297260
## iter 250 value 599.588594
## iter 260 value 597.787583
## iter 270 value 595.135891
## iter 280 value 585.022136
## iter 290 value 575.466559
## iter 300 value 570.159954
## iter 310 value 562.084254
## iter 320 value 552.930719
## iter 330 value 545.582173
## iter 340 value 540.122187
## iter 350 value 535.265294
## iter 360 value 532.628995
## iter 370 value 530.071624
## iter 380 value 528.060579
## iter 390 value 526.262586
## iter 400 value 525.339962
## iter 410 value 524.892119
## iter 420 value 524.023242
## iter 430 value 522.718460
## iter 440 value 522.069341
## iter 450 value 521.529123
## iter 460 value 521.346000
## iter 470 value 521.324263
## iter 480 value 521.314622
## iter 490 value 521.309971
## iter 500 value 521.307539
## final  value 521.307539 
## stopped after 500 iterations
## # weights:  15
## initial  value 1432508.485107 
## iter  10 value 5339.346468
## iter  20 value 3559.675302
## iter  30 value 2226.302987
## iter  40 value 1816.054506
## iter  50 value 1775.609031
## iter  60 value 1743.468383
## iter  70 value 1735.108816
## iter  80 value 1730.896427
## iter  90 value 1641.715324
## iter 100 value 1501.577674
## iter 110 value 1427.786525
## iter 120 value 1373.888751
## iter 130 value 1267.663270
## iter 140 value 1187.856222
## iter 150 value 1180.076137
## iter 160 value 1172.141917
## iter 170 value 1167.677887
## iter 180 value 1167.273107
## iter 190 value 1165.696008
## iter 200 value 1164.762732
## iter 210 value 1164.669219
## iter 220 value 1164.615670
## iter 230 value 1164.592541
## final  value 1164.591750 
## converged
## # weights:  36
## initial  value 1388708.885180 
## iter  10 value 5973.882990
## iter  20 value 4403.024030
## iter  30 value 3904.147124
## iter  40 value 3212.204664
## iter  50 value 2719.799176
## iter  60 value 2223.515068
## iter  70 value 2096.897718
## iter  80 value 1989.025444
## iter  90 value 1904.602705
## iter 100 value 1474.363221
## iter 110 value 1399.616047
## iter 120 value 1350.869603
## iter 130 value 1317.414053
## iter 140 value 1279.222464
## iter 150 value 1260.784377
## iter 160 value 1245.351813
## iter 170 value 1182.713357
## iter 180 value 1159.840787
## iter 190 value 1122.465503
## iter 200 value 1097.101789
## iter 210 value 1088.784953
## iter 220 value 1080.037009
## iter 230 value 1074.656202
## iter 240 value 1051.272103
## iter 250 value 1011.099836
## iter 260 value 998.719834
## iter 270 value 992.754856
## iter 280 value 970.061953
## iter 290 value 963.994104
## iter 300 value 958.076888
## iter 310 value 957.154108
## iter 320 value 956.992009
## final  value 956.960470 
## converged
## # weights:  71
## initial  value 1414060.565511 
## iter  10 value 1470.441608
## iter  20 value 1140.396857
## iter  30 value 1005.112034
## iter  40 value 944.503247
## iter  50 value 879.403398
## iter  60 value 830.461347
## iter  70 value 766.620792
## iter  80 value 725.179608
## iter  90 value 703.923690
## iter 100 value 686.921141
## iter 110 value 681.456410
## iter 120 value 671.277401
## iter 130 value 663.666234
## iter 140 value 657.604683
## iter 150 value 651.569128
## iter 160 value 648.109202
## iter 170 value 642.751676
## iter 180 value 637.114922
## iter 190 value 625.724848
## iter 200 value 618.017962
## iter 210 value 601.061879
## iter 220 value 586.780671
## iter 230 value 577.647206
## iter 240 value 572.822859
## iter 250 value 570.184695
## iter 260 value 567.304796
## iter 270 value 565.373935
## iter 280 value 562.558281
## iter 290 value 554.774704
## iter 300 value 554.106164
## iter 310 value 552.564583
## iter 320 value 551.050081
## iter 330 value 549.565636
## iter 340 value 547.767807
## iter 350 value 547.108280
## iter 360 value 546.821199
## iter 370 value 546.703356
## iter 380 value 546.650465
## iter 390 value 546.637810
## iter 400 value 546.636447
## final  value 546.636159 
## converged
## # weights:  106
## initial  value 1417058.631998 
## iter  10 value 1460.257960
## iter  20 value 1093.108040
## iter  30 value 968.028515
## iter  40 value 888.312314
## iter  50 value 810.182400
## iter  60 value 724.166239
## iter  70 value 660.608056
## iter  80 value 614.044278
## iter  90 value 585.511991
## iter 100 value 550.889973
## iter 110 value 513.191518
## iter 120 value 485.544687
## iter 130 value 474.783283
## iter 140 value 463.717000
## iter 150 value 456.037892
## iter 160 value 451.277237
## iter 170 value 446.841479
## iter 180 value 440.803120
## iter 190 value 436.815671
## iter 200 value 431.965020
## iter 210 value 423.414354
## iter 220 value 421.376213
## iter 230 value 420.192321
## iter 240 value 418.580848
## iter 250 value 415.450995
## iter 260 value 412.227896
## iter 270 value 408.006856
## iter 280 value 405.862199
## iter 290 value 403.089948
## iter 300 value 401.025740
## iter 310 value 400.168990
## iter 320 value 399.343895
## iter 330 value 399.174172
## iter 340 value 399.033031
## iter 350 value 398.901916
## iter 360 value 398.829082
## iter 370 value 398.803913
## iter 380 value 398.793240
## iter 390 value 398.788796
## iter 400 value 398.786717
## iter 410 value 398.786553
## final  value 398.786536 
## converged
## # weights:  141
## initial  value 1355818.693137 
## iter  10 value 1360.133812
## iter  20 value 984.913011
## iter  30 value 845.216891
## iter  40 value 768.562998
## iter  50 value 719.519982
## iter  60 value 672.602200
## iter  70 value 643.234698
## iter  80 value 597.843307
## iter  90 value 558.094847
## iter 100 value 526.895491
## iter 110 value 503.801467
## iter 120 value 477.390878
## iter 130 value 452.356606
## iter 140 value 414.039424
## iter 150 value 394.428138
## iter 160 value 376.466515
## iter 170 value 359.765287
## iter 180 value 341.864542
## iter 190 value 325.501222
## iter 200 value 313.291401
## iter 210 value 299.809674
## iter 220 value 291.764776
## iter 230 value 284.561389
## iter 240 value 278.425721
## iter 250 value 273.471175
## iter 260 value 269.552468
## iter 270 value 266.868983
## iter 280 value 264.438317
## iter 290 value 263.367642
## iter 300 value 262.827835
## iter 310 value 262.043976
## iter 320 value 260.800474
## iter 330 value 258.940403
## iter 340 value 256.411733
## iter 350 value 254.606692
## iter 360 value 252.353112
## iter 370 value 247.924148
## iter 380 value 243.943318
## iter 390 value 241.384897
## iter 400 value 238.193853
## iter 410 value 235.313416
## iter 420 value 233.479646
## iter 430 value 232.330667
## iter 440 value 230.968516
## iter 450 value 227.737917
## iter 460 value 225.454505
## iter 470 value 223.863862
## iter 480 value 222.110833
## iter 490 value 220.282719
## iter 500 value 218.407044
## final  value 218.407044 
## stopped after 500 iterations
## # weights:  15
## initial  value 1389878.342298 
## iter  10 value 11181.065855
## iter  20 value 4652.209575
## iter  30 value 1886.737413
## iter  40 value 1832.986654
## iter  50 value 1687.531635
## iter  60 value 1634.128802
## iter  70 value 1569.450801
## iter  80 value 1548.248073
## iter  90 value 1514.846917
## iter 100 value 1504.965066
## iter 110 value 1504.875721
## iter 120 value 1496.533524
## iter 130 value 1489.277662
## iter 140 value 1471.388744
## iter 150 value 1431.681654
## iter 160 value 1386.996789
## iter 170 value 1349.824249
## iter 180 value 1263.141703
## iter 190 value 1211.903138
## iter 200 value 1191.422154
## iter 210 value 1160.507839
## final  value 1159.227722 
## converged
## # weights:  36
## initial  value 1379452.138405 
## iter  10 value 470956.100085
## iter  20 value 6956.222637
## iter  30 value 5359.511024
## iter  40 value 4540.502955
## iter  50 value 3698.589962
## iter  60 value 2777.832924
## iter  70 value 2162.238570
## iter  80 value 1734.382695
## iter  90 value 1724.536820
## iter 100 value 1722.141507
## iter 110 value 1715.523082
## iter 120 value 1698.836358
## iter 130 value 1692.688275
## iter 140 value 1689.348280
## iter 150 value 1686.740853
## iter 160 value 1682.580131
## iter 170 value 1676.929521
## iter 180 value 1676.432112
## iter 190 value 1674.278057
## iter 200 value 1658.926781
## iter 210 value 1566.904285
## iter 220 value 1555.100142
## iter 230 value 1553.367109
## iter 240 value 1540.398685
## iter 250 value 1488.396689
## iter 260 value 1449.493516
## iter 270 value 1444.205081
## iter 280 value 1442.545355
## iter 290 value 1441.052094
## iter 300 value 1439.595921
## iter 310 value 1438.086840
## iter 320 value 1438.058631
## iter 330 value 1437.845194
## iter 340 value 1436.132884
## iter 350 value 1418.804879
## iter 360 value 1414.719343
## iter 370 value 1409.272767
## iter 380 value 1396.531143
## iter 390 value 1392.307854
## iter 400 value 1388.294514
## iter 410 value 1366.498189
## iter 420 value 1358.983915
## iter 430 value 1356.562243
## iter 440 value 1352.793078
## iter 450 value 1349.863578
## iter 460 value 1299.130272
## iter 470 value 1166.562136
## iter 480 value 1123.299296
## iter 490 value 1090.519466
## iter 500 value 1075.379096
## final  value 1075.379096 
## stopped after 500 iterations
## # weights:  71
## initial  value 1404574.992625 
## iter  10 value 2497.312276
## iter  20 value 1689.574488
## iter  30 value 1259.310928
## iter  40 value 1128.798406
## iter  50 value 1063.422824
## iter  60 value 1011.441808
## iter  70 value 971.837579
## iter  80 value 946.850245
## iter  90 value 906.483215
## iter 100 value 873.848935
## iter 110 value 854.103426
## iter 120 value 847.784582
## iter 130 value 842.280725
## iter 140 value 841.081323
## iter 150 value 840.352983
## iter 160 value 838.557513
## iter 170 value 837.172486
## iter 180 value 821.329071
## iter 190 value 813.718188
## iter 200 value 811.922614
## iter 210 value 811.658024
## iter 220 value 810.350544
## iter 230 value 809.029618
## iter 240 value 806.312388
## iter 250 value 803.894094
## iter 260 value 802.692457
## iter 270 value 802.145239
## iter 280 value 801.811403
## iter 290 value 800.728849
## iter 300 value 799.715453
## iter 310 value 797.265741
## iter 320 value 796.334695
## iter 330 value 792.225449
## iter 340 value 786.719803
## iter 350 value 782.655681
## iter 360 value 781.642959
## iter 370 value 781.118547
## iter 380 value 780.979391
## iter 390 value 778.732192
## iter 400 value 772.373079
## iter 410 value 718.860711
## iter 420 value 684.829198
## iter 430 value 676.516006
## iter 440 value 671.032349
## iter 450 value 666.026938
## iter 460 value 663.067863
## iter 470 value 656.063977
## iter 480 value 642.541912
## iter 490 value 640.109619
## iter 500 value 637.287794
## final  value 637.287794 
## stopped after 500 iterations
## # weights:  106
## initial  value 1466744.576828 
## iter  10 value 1949.525647
## iter  20 value 1198.145707
## iter  30 value 961.891397
## iter  40 value 847.673786
## iter  50 value 796.717697
## iter  60 value 761.732104
## iter  70 value 710.039563
## iter  80 value 677.077790
## iter  90 value 648.954457
## iter 100 value 611.278283
## iter 110 value 563.949743
## iter 120 value 520.557059
## iter 130 value 481.934659
## iter 140 value 464.222183
## iter 150 value 442.714867
## iter 160 value 428.005115
## iter 170 value 414.340844
## iter 180 value 397.558993
## iter 190 value 389.789495
## iter 200 value 377.200348
## iter 210 value 361.856334
## iter 220 value 355.252554
## iter 230 value 351.741806
## iter 240 value 349.173553
## iter 250 value 343.660082
## iter 260 value 339.752645
## iter 270 value 336.071421
## iter 280 value 331.616693
## iter 290 value 326.269968
## iter 300 value 320.467133
## iter 310 value 314.585526
## iter 320 value 308.931265
## iter 330 value 305.664808
## iter 340 value 302.279450
## iter 350 value 297.035884
## iter 360 value 292.466895
## iter 370 value 288.960623
## iter 380 value 286.018169
## iter 390 value 282.697154
## iter 400 value 281.051803
## iter 410 value 279.663892
## iter 420 value 277.222534
## iter 430 value 275.493348
## iter 440 value 275.028082
## iter 450 value 274.528571
## iter 460 value 273.813109
## iter 470 value 272.864676
## iter 480 value 272.308980
## iter 490 value 271.580594
## iter 500 value 270.698108
## final  value 270.698108 
## stopped after 500 iterations
## # weights:  141
## initial  value 1374891.404402 
## iter  10 value 1386.133672
## iter  20 value 1071.423448
## iter  30 value 886.405820
## iter  40 value 788.568656
## iter  50 value 713.049041
## iter  60 value 649.142906
## iter  70 value 595.732174
## iter  80 value 560.402695
## iter  90 value 529.742159
## iter 100 value 488.734416
## iter 110 value 452.821597
## iter 120 value 418.190348
## iter 130 value 388.482839
## iter 140 value 355.679908
## iter 150 value 336.292547
## iter 160 value 325.961251
## iter 170 value 319.447185
## iter 180 value 313.792063
## iter 190 value 305.167562
## iter 200 value 294.197673
## iter 210 value 283.407489
## iter 220 value 271.631438
## iter 230 value 265.213780
## iter 240 value 258.833650
## iter 250 value 253.291952
## iter 260 value 246.814530
## iter 270 value 242.105879
## iter 280 value 237.703470
## iter 290 value 235.122473
## iter 300 value 234.078559
## iter 310 value 231.557911
## iter 320 value 227.998954
## iter 330 value 223.979351
## iter 340 value 222.409500
## iter 350 value 221.199388
## iter 360 value 218.930773
## iter 370 value 216.556544
## iter 380 value 215.232716
## iter 390 value 213.965555
## iter 400 value 212.547285
## iter 410 value 209.757112
## iter 420 value 207.095307
## iter 430 value 205.701826
## iter 440 value 204.638510
## iter 450 value 204.291678
## iter 460 value 203.751611
## iter 470 value 202.927265
## iter 480 value 202.249639
## iter 490 value 202.034761
## iter 500 value 201.815602
## final  value 201.815602 
## stopped after 500 iterations
## # weights:  15
## initial  value 1420774.165213 
## iter  10 value 6414.129254
## iter  20 value 5813.352589
## iter  30 value 4862.960337
## iter  40 value 3268.292716
## iter  50 value 2462.536656
## iter  60 value 1611.251157
## iter  70 value 1370.345345
## iter  80 value 1342.294010
## iter  90 value 1285.429562
## iter 100 value 1224.400646
## iter 110 value 1209.957789
## iter 120 value 1175.414182
## iter 130 value 1162.775154
## iter 140 value 1162.122544
## iter 150 value 1161.279417
## iter 160 value 1158.898149
## iter 170 value 1158.601927
## iter 180 value 1158.543898
## iter 190 value 1157.753860
## iter 200 value 1157.544140
## iter 210 value 1157.537344
## iter 220 value 1157.391520
## iter 230 value 1157.294973
## final  value 1157.294902 
## converged
## # weights:  36
## initial  value 1417826.813888 
## iter  10 value 5918.596206
## iter  20 value 3038.662120
## iter  30 value 1932.067251
## iter  40 value 1392.560625
## iter  50 value 1254.338043
## iter  60 value 1198.402287
## iter  70 value 1177.932116
## iter  80 value 1171.554781
## iter  90 value 1161.518735
## iter 100 value 1150.224216
## iter 110 value 1119.589898
## iter 120 value 1092.283455
## iter 130 value 1080.965825
## iter 140 value 1080.146157
## iter 150 value 1079.263465
## iter 160 value 1078.992786
## iter 170 value 1078.980893
## iter 180 value 1078.896782
## iter 180 value 1078.896771
## final  value 1078.896771 
## converged
## # weights:  71
## initial  value 1415081.982814 
## iter  10 value 3553.481708
## iter  20 value 2218.502935
## iter  30 value 1404.725219
## iter  40 value 1145.963674
## iter  50 value 995.315998
## iter  60 value 945.009644
## iter  70 value 914.582198
## iter  80 value 896.400935
## iter  90 value 861.382400
## iter 100 value 821.374482
## iter 110 value 802.074961
## iter 120 value 778.199624
## iter 130 value 751.993940
## iter 140 value 719.141515
## iter 150 value 701.109762
## iter 160 value 687.569444
## iter 170 value 681.053937
## iter 180 value 663.516120
## iter 190 value 648.571265
## iter 200 value 633.201311
## iter 210 value 624.321667
## iter 220 value 623.033257
## iter 230 value 620.209512
## iter 240 value 614.721877
## iter 250 value 602.972520
## iter 260 value 591.362749
## iter 270 value 576.650362
## iter 280 value 570.809524
## iter 290 value 565.836208
## iter 300 value 564.307827
## iter 310 value 563.653259
## iter 320 value 563.306345
## iter 330 value 562.820319
## iter 340 value 561.735426
## iter 350 value 560.613991
## iter 360 value 560.147472
## iter 370 value 560.002743
## iter 380 value 559.716511
## iter 390 value 559.333161
## iter 400 value 557.435441
## iter 410 value 554.124171
## iter 420 value 552.948816
## iter 430 value 552.167626
## iter 440 value 551.193828
## iter 450 value 550.358800
## iter 460 value 549.626636
## iter 470 value 549.417577
## iter 480 value 548.160726
## iter 490 value 546.687296
## iter 500 value 544.842008
## final  value 544.842008 
## stopped after 500 iterations
## # weights:  106
## initial  value 1439929.964485 
## iter  10 value 1552.564060
## iter  20 value 1034.583771
## iter  30 value 895.904341
## iter  40 value 823.484717
## iter  50 value 785.700622
## iter  60 value 742.081468
## iter  70 value 692.144515
## iter  80 value 644.230124
## iter  90 value 595.615102
## iter 100 value 560.766592
## iter 110 value 541.681626
## iter 120 value 532.900776
## iter 130 value 518.291685
## iter 140 value 497.537895
## iter 150 value 477.990624
## iter 160 value 470.955859
## iter 170 value 463.866921
## iter 180 value 455.997754
## iter 190 value 449.672433
## iter 200 value 444.539951
## iter 210 value 439.783795
## iter 220 value 436.667943
## iter 230 value 434.499993
## iter 240 value 431.391308
## iter 250 value 425.395176
## iter 260 value 415.017648
## iter 270 value 408.845264
## iter 280 value 399.121104
## iter 290 value 388.247129
## iter 300 value 377.726577
## iter 310 value 365.691521
## iter 320 value 359.743445
## iter 330 value 351.115545
## iter 340 value 348.127766
## iter 350 value 346.848095
## iter 360 value 346.278323
## iter 370 value 345.442394
## iter 380 value 344.085153
## iter 390 value 341.850814
## iter 400 value 339.782062
## iter 410 value 337.568525
## iter 420 value 336.456192
## iter 430 value 336.173986
## iter 440 value 336.118187
## iter 450 value 335.931237
## iter 460 value 335.705107
## iter 470 value 335.416426
## iter 480 value 335.180318
## iter 490 value 335.004578
## iter 500 value 334.906565
## final  value 334.906565 
## stopped after 500 iterations
## # weights:  141
## initial  value 1429753.188883 
## iter  10 value 1845.074529
## iter  20 value 1256.776382
## iter  30 value 974.823715
## iter  40 value 794.899919
## iter  50 value 676.207072
## iter  60 value 593.552884
## iter  70 value 540.239905
## iter  80 value 502.057017
## iter  90 value 469.203647
## iter 100 value 446.107793
## iter 110 value 426.330964
## iter 120 value 399.815076
## iter 130 value 368.600279
## iter 140 value 350.493586
## iter 150 value 336.026535
## iter 160 value 325.873069
## iter 170 value 313.629731
## iter 180 value 304.260675
## iter 190 value 291.225604
## iter 200 value 280.377767
## iter 210 value 270.221988
## iter 220 value 261.465133
## iter 230 value 255.101086
## iter 240 value 248.376606
## iter 250 value 243.256804
## iter 260 value 240.047533
## iter 270 value 238.467678
## iter 280 value 236.606733
## iter 290 value 235.795310
## iter 300 value 235.417793
## iter 310 value 234.771051
## iter 320 value 233.952330
## iter 330 value 233.222352
## iter 340 value 232.717339
## iter 350 value 232.033101
## iter 360 value 231.832153
## iter 370 value 230.289278
## iter 380 value 228.158805
## iter 390 value 227.609289
## iter 400 value 227.104155
## iter 410 value 226.917557
## iter 420 value 226.548537
## iter 430 value 226.348796
## iter 440 value 225.840303
## iter 450 value 225.480877
## iter 460 value 225.069630
## iter 470 value 224.493583
## iter 480 value 222.455905
## iter 490 value 221.718653
## iter 500 value 221.511772
## final  value 221.511772 
## stopped after 500 iterations
## # weights:  15
## initial  value 1425529.190665 
## iter  10 value 4416.080235
## iter  20 value 2580.906070
## iter  30 value 2013.762357
## iter  40 value 1756.856814
## iter  50 value 1567.975340
## iter  60 value 1524.131971
## iter  70 value 1513.146891
## iter  80 value 1470.899508
## iter  90 value 1452.443466
## iter 100 value 1451.593458
## iter 110 value 1442.033052
## iter 120 value 1436.205828
## iter 130 value 1433.485099
## iter 140 value 1427.316952
## iter 150 value 1418.005591
## iter 160 value 1417.434184
## iter 170 value 1415.371879
## iter 180 value 1408.763299
## iter 190 value 1406.979583
## iter 200 value 1405.451359
## iter 210 value 1403.068598
## iter 220 value 1402.639879
## iter 230 value 1401.667912
## iter 240 value 1399.913617
## iter 250 value 1399.515880
## iter 260 value 1399.077903
## iter 270 value 1397.063469
## iter 280 value 1396.246454
## iter 290 value 1396.090051
## iter 300 value 1394.051409
## iter 310 value 1393.631807
## iter 320 value 1393.613246
## iter 330 value 1391.605102
## iter 340 value 1390.320633
## iter 350 value 1390.280897
## iter 360 value 1388.864965
## iter 370 value 1388.391708
## iter 380 value 1388.366859
## iter 390 value 1386.658890
## iter 400 value 1384.122573
## iter 410 value 1384.068346
## iter 420 value 1383.846701
## iter 430 value 1381.373633
## iter 440 value 1381.113246
## iter 450 value 1380.923580
## iter 460 value 1379.772253
## iter 470 value 1379.374598
## iter 480 value 1379.272724
## iter 490 value 1377.977934
## iter 500 value 1377.628995
## final  value 1377.628995 
## stopped after 500 iterations
## # weights:  36
## initial  value 1372062.450316 
## iter  10 value 3559.658349
## iter  20 value 2090.046609
## iter  30 value 1731.799937
## iter  40 value 1344.075155
## iter  50 value 1042.843572
## iter  60 value 974.158913
## iter  70 value 940.090271
## iter  80 value 927.231012
## iter  90 value 923.797585
## iter 100 value 922.955598
## iter 110 value 919.298232
## iter 120 value 913.795230
## iter 130 value 911.027014
## iter 140 value 910.111965
## iter 150 value 909.680583
## iter 160 value 909.019314
## iter 170 value 908.953307
## iter 180 value 908.647608
## iter 190 value 907.734640
## iter 200 value 904.242337
## iter 210 value 898.658798
## iter 220 value 896.172841
## iter 230 value 893.840650
## iter 240 value 892.851794
## iter 250 value 891.931406
## iter 260 value 890.738872
## iter 270 value 887.271600
## iter 280 value 882.665150
## iter 290 value 876.132158
## iter 300 value 864.687189
## iter 310 value 860.714472
## iter 320 value 857.555540
## iter 330 value 851.384141
## iter 340 value 846.076738
## iter 350 value 844.037233
## iter 360 value 841.058271
## iter 370 value 828.808962
## iter 380 value 818.559954
## iter 390 value 815.501696
## iter 400 value 812.560558
## iter 410 value 808.412470
## iter 420 value 802.282239
## iter 430 value 795.428137
## iter 440 value 791.523508
## iter 450 value 788.205393
## iter 460 value 787.715134
## iter 470 value 787.068854
## iter 480 value 786.258573
## iter 490 value 785.440203
## iter 500 value 771.678847
## final  value 771.678847 
## stopped after 500 iterations
## # weights:  71
## initial  value 1407405.516296 
## iter  10 value 1285.530881
## iter  20 value 1081.169193
## iter  30 value 1005.220023
## iter  40 value 968.939347
## iter  50 value 913.203922
## iter  60 value 853.491426
## iter  70 value 828.000135
## iter  80 value 798.080177
## iter  90 value 754.917918
## iter 100 value 724.539230
## iter 110 value 703.536193
## iter 120 value 685.329172
## iter 130 value 672.192954
## iter 140 value 661.594242
## iter 150 value 645.578389
## iter 160 value 636.379213
## iter 170 value 620.218254
## iter 180 value 601.765717
## iter 190 value 584.032641
## iter 200 value 560.296777
## iter 210 value 540.909314
## iter 220 value 524.205570
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## iter 240 value 497.868114
## iter 250 value 474.108482
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## iter 270 value 436.961277
## iter 280 value 431.639082
## iter 290 value 430.085224
## iter 300 value 429.576435
## iter 310 value 428.907065
## iter 320 value 428.483589
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## iter 340 value 427.379720
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## iter 400 value 424.608437
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## iter 430 value 424.332825
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## iter 450 value 424.118919
## iter 460 value 424.104702
## iter 470 value 424.087131
## iter 480 value 424.081078
## iter 490 value 424.071059
## iter 500 value 424.058395
## final  value 424.058395 
## stopped after 500 iterations
## # weights:  106
## initial  value 1424814.260133 
## iter  10 value 1594.538846
## iter  20 value 1102.098337
## iter  30 value 991.671078
## iter  40 value 893.912976
## iter  50 value 862.575916
## iter  60 value 809.624436
## iter  70 value 729.349119
## iter  80 value 671.163613
## iter  90 value 626.477590
## iter 100 value 594.128246
## iter 110 value 561.441133
## iter 120 value 533.954674
## iter 130 value 518.786171
## iter 140 value 507.558350
## iter 150 value 497.010362
## iter 160 value 487.370351
## iter 170 value 475.418923
## iter 180 value 462.441269
## iter 190 value 450.688995
## iter 200 value 434.668248
## iter 210 value 419.718570
## iter 220 value 414.562902
## iter 230 value 409.408949
## iter 240 value 399.930681
## iter 250 value 390.923710
## iter 260 value 372.793727
## iter 270 value 357.867706
## iter 280 value 349.582949
## iter 290 value 342.621101
## iter 300 value 335.503580
## iter 310 value 329.318959
## iter 320 value 322.076818
## iter 330 value 316.336668
## iter 340 value 312.642786
## iter 350 value 310.383316
## iter 360 value 308.764523
## iter 370 value 307.190392
## iter 380 value 306.120662
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## iter 400 value 303.247248
## iter 410 value 301.866282
## iter 420 value 301.447400
## iter 430 value 301.216995
## iter 440 value 301.173436
## iter 450 value 301.102476
## iter 460 value 301.025853
## iter 470 value 300.919934
## iter 480 value 300.843856
## iter 490 value 300.785988
## iter 500 value 300.743492
## final  value 300.743492 
## stopped after 500 iterations
## # weights:  141
## initial  value 1374718.962443 
## iter  10 value 3867.947258
## iter  20 value 1181.418014
## iter  30 value 917.745772
## iter  40 value 730.277187
## iter  50 value 629.976521
## iter  60 value 560.904120
## iter  70 value 508.384994
## iter  80 value 479.468314
## iter  90 value 455.683099
## iter 100 value 433.409400
## iter 110 value 406.792641
## iter 120 value 389.212414
## iter 130 value 370.770323
## iter 140 value 360.463879
## iter 150 value 352.582510
## iter 160 value 347.151858
## iter 170 value 342.584044
## iter 180 value 338.536125
## iter 190 value 333.363048
## iter 200 value 331.222135
## iter 210 value 328.358136
## iter 220 value 325.539217
## iter 230 value 321.973662
## iter 240 value 316.170390
## iter 250 value 309.502115
## iter 260 value 301.747857
## iter 270 value 297.624084
## iter 280 value 294.009404
## iter 290 value 292.743939
## iter 300 value 292.161368
## iter 310 value 291.300523
## iter 320 value 289.169547
## iter 330 value 287.357796
## iter 340 value 285.450748
## iter 350 value 282.538819
## iter 360 value 279.754132
## iter 370 value 274.362949
## iter 380 value 270.195928
## iter 390 value 265.590122
## iter 400 value 261.111312
## iter 410 value 255.563835
## iter 420 value 250.297615
## iter 430 value 246.809215
## iter 440 value 244.839308
## iter 450 value 243.157614
## iter 460 value 241.861797
## iter 470 value 240.940082
## iter 480 value 240.353628
## iter 490 value 239.881087
## iter 500 value 239.169566
## final  value 239.169566 
## stopped after 500 iterations
## # weights:  15
## initial  value 1431873.437992 
## iter  10 value 11878.146060
## iter  20 value 6578.310020
## iter  30 value 5704.810366
## iter  40 value 3808.295340
## iter  50 value 2442.939464
## iter  60 value 1776.195343
## iter  70 value 1579.653803
## iter  80 value 1495.946338
## iter  90 value 1473.417703
## iter 100 value 1416.139657
## iter 110 value 1396.739612
## iter 120 value 1394.396687
## iter 130 value 1394.189830
## final  value 1394.189776 
## converged
## # weights:  36
## initial  value 1430331.198013 
## iter  10 value 18676.235133
## iter  20 value 9513.110260
## iter  30 value 6750.324980
## iter  40 value 4806.755940
## iter  50 value 3614.906157
## iter  60 value 2645.032578
## iter  70 value 2233.237310
## iter  80 value 1948.274621
## iter  90 value 1765.058969
## iter 100 value 1620.401578
## iter 110 value 1431.533548
## iter 120 value 1344.381896
## iter 130 value 1295.481241
## iter 140 value 1260.349687
## iter 150 value 1234.615332
## iter 160 value 1215.679553
## iter 170 value 1198.309315
## iter 180 value 1155.842502
## iter 190 value 1134.255219
## iter 200 value 1125.618284
## iter 210 value 1124.785430
## iter 220 value 1124.672863
## iter 230 value 1124.632976
## iter 240 value 1124.590404
## iter 250 value 1124.495576
## iter 260 value 1124.429942
## final  value 1124.429772 
## converged
## # weights:  71
## initial  value 1368000.667055 
## iter  10 value 1850.053102
## iter  20 value 1265.692526
## iter  30 value 1171.863894
## iter  40 value 1118.397806
## iter  50 value 1080.917850
## iter  60 value 1036.436256
## iter  70 value 1009.061296
## iter  80 value 987.113551
## iter  90 value 971.971624
## iter 100 value 959.849128
## iter 110 value 949.056143
## iter 120 value 935.716173
## iter 130 value 926.788659
## iter 140 value 919.760793
## iter 150 value 915.049127
## iter 160 value 910.306834
## iter 170 value 903.357062
## iter 180 value 896.366872
## iter 190 value 893.761901
## iter 200 value 892.293931
## iter 210 value 890.109988
## iter 220 value 884.053138
## iter 230 value 880.127699
## iter 240 value 879.424549
## iter 250 value 879.231307
## iter 260 value 879.217898
## final  value 879.217409 
## converged
## # weights:  106
## initial  value 1351450.994197 
## iter  10 value 1383.054146
## iter  20 value 1147.076492
## iter  30 value 1023.484655
## iter  40 value 929.947252
## iter  50 value 877.216886
## iter  60 value 848.801645
## iter  70 value 810.846709
## iter  80 value 781.267550
## iter  90 value 763.936016
## iter 100 value 754.845719
## iter 110 value 732.981916
## iter 120 value 695.279141
## iter 130 value 674.587016
## iter 140 value 664.015421
## iter 150 value 658.033951
## iter 160 value 652.472670
## iter 170 value 646.477523
## iter 180 value 643.892102
## iter 190 value 638.846179
## iter 200 value 633.025087
## iter 210 value 631.224742
## iter 220 value 629.418101
## iter 230 value 627.069737
## iter 240 value 623.763129
## iter 250 value 621.789205
## iter 260 value 620.498994
## iter 270 value 619.723572
## iter 280 value 619.323303
## iter 290 value 619.022169
## iter 300 value 618.753879
## iter 310 value 618.635870
## iter 320 value 618.611610
## final  value 618.605267 
## converged
## # weights:  141
## initial  value 1414573.195862 
## iter  10 value 1738.543625
## iter  20 value 1175.618192
## iter  30 value 1038.700648
## iter  40 value 987.785092
## iter  50 value 913.653123
## iter  60 value 855.534047
## iter  70 value 810.916695
## iter  80 value 779.537571
## iter  90 value 748.333623
## iter 100 value 720.801955
## iter 110 value 697.199757
## iter 120 value 680.627682
## iter 130 value 665.197338
## iter 140 value 648.901316
## iter 150 value 631.947465
## iter 160 value 623.766745
## iter 170 value 617.762456
## iter 180 value 610.083020
## iter 190 value 598.965711
## iter 200 value 589.640915
## iter 210 value 584.270266
## iter 220 value 581.519352
## iter 230 value 579.075103
## iter 240 value 577.163564
## iter 250 value 576.039855
## iter 260 value 574.916167
## iter 270 value 572.315351
## iter 280 value 569.811148
## iter 290 value 568.816832
## iter 300 value 567.639118
## iter 310 value 565.983362
## iter 320 value 563.903763
## iter 330 value 561.236246
## iter 340 value 557.659080
## iter 350 value 553.892787
## iter 360 value 549.182778
## iter 370 value 540.612877
## iter 380 value 534.731396
## iter 390 value 530.054984
## iter 400 value 525.767070
## iter 410 value 523.294809
## iter 420 value 519.093696
## iter 430 value 516.317632
## iter 440 value 515.010576
## iter 450 value 514.337925
## iter 460 value 513.968352
## iter 470 value 513.805914
## iter 480 value 513.767460
## iter 490 value 513.752568
## iter 500 value 513.748522
## final  value 513.748522 
## stopped after 500 iterations
## # weights:  15
## initial  value 1389376.583573 
## iter  10 value 42513.885861
## iter  20 value 7760.089986
## iter  30 value 4647.309785
## iter  40 value 2821.511176
## iter  50 value 1697.598772
## iter  60 value 1416.596018
## iter  70 value 1350.818268
## iter  80 value 1264.669605
## iter  90 value 1194.266874
## iter 100 value 1165.181000
## iter 110 value 1143.335251
## iter 120 value 1128.393794
## iter 130 value 1124.758777
## iter 140 value 1123.639926
## iter 150 value 1121.896772
## iter 160 value 1121.493406
## iter 170 value 1121.321700
## iter 180 value 1121.142484
## final  value 1121.130155 
## converged
## # weights:  36
## initial  value 1426640.918311 
## iter  10 value 305748.306450
## iter  20 value 201935.940094
## iter  30 value 7851.023625
## iter  40 value 5881.046265
## iter  50 value 5626.968655
## iter  60 value 5443.310648
## iter  70 value 3891.670765
## iter  80 value 3058.910714
## iter  90 value 2519.763894
## iter 100 value 2010.773381
## iter 110 value 1834.282124
## iter 120 value 1707.739772
## iter 130 value 1439.368209
## iter 140 value 1398.847167
## iter 150 value 1316.421473
## iter 160 value 1297.961909
## iter 170 value 1205.379355
## iter 180 value 1144.416753
## iter 190 value 1103.867625
## iter 200 value 1080.196190
## iter 210 value 1045.569477
## iter 220 value 1030.168361
## iter 230 value 1027.902224
## iter 240 value 1024.874845
## iter 250 value 1018.938487
## iter 260 value 1009.808285
## iter 270 value 997.216460
## iter 280 value 966.500811
## iter 290 value 938.936122
## iter 300 value 930.124601
## iter 310 value 924.429835
## iter 320 value 918.416532
## iter 330 value 912.237135
## iter 340 value 907.474656
## iter 350 value 906.090403
## iter 360 value 904.053334
## iter 370 value 903.172606
## iter 380 value 903.021779
## iter 390 value 902.756015
## iter 400 value 902.220670
## iter 410 value 902.100105
## iter 420 value 902.080627
## iter 430 value 902.075992
## final  value 902.075688 
## converged
## # weights:  71
## initial  value 1394268.565642 
## iter  10 value 1873.712711
## iter  20 value 1317.003912
## iter  30 value 1094.609181
## iter  40 value 999.750983
## iter  50 value 937.663120
## iter  60 value 906.037934
## iter  70 value 872.826445
## iter  80 value 844.494706
## iter  90 value 803.824136
## iter 100 value 748.359752
## iter 110 value 738.653201
## iter 120 value 721.366946
## iter 130 value 705.023144
## iter 140 value 698.711951
## iter 150 value 697.537037
## iter 160 value 697.010131
## iter 170 value 694.681049
## iter 180 value 688.978444
## iter 190 value 682.577655
## iter 200 value 678.197417
## iter 210 value 670.835703
## iter 220 value 664.482069
## iter 230 value 660.525414
## iter 240 value 657.694948
## iter 250 value 654.533808
## iter 260 value 652.340445
## iter 270 value 650.125552
## iter 280 value 649.415338
## iter 290 value 649.246118
## iter 300 value 649.204906
## iter 310 value 649.168164
## iter 320 value 649.116135
## iter 330 value 649.026350
## iter 340 value 648.852544
## iter 350 value 648.744976
## iter 360 value 648.708102
## iter 370 value 648.675190
## iter 380 value 648.670736
## iter 390 value 648.669079
## final  value 648.668996 
## converged
## # weights:  106
## initial  value 1417638.523158 
## iter  10 value 1795.865423
## iter  20 value 1006.698836
## iter  30 value 879.507381
## iter  40 value 799.718596
## iter  50 value 747.307981
## iter  60 value 716.944440
## iter  70 value 651.785899
## iter  80 value 591.141017
## iter  90 value 559.055881
## iter 100 value 521.956807
## iter 110 value 485.087138
## iter 120 value 459.083686
## iter 130 value 427.732516
## iter 140 value 411.810075
## iter 150 value 394.458348
## iter 160 value 384.757527
## iter 170 value 372.763265
## iter 180 value 362.352760
## iter 190 value 346.996447
## iter 200 value 340.624309
## iter 210 value 335.872534
## iter 220 value 333.068989
## iter 230 value 330.730462
## iter 240 value 327.965220
## iter 250 value 323.747396
## iter 260 value 317.945625
## iter 270 value 314.161351
## iter 280 value 310.872620
## iter 290 value 306.041003
## iter 300 value 300.063982
## iter 310 value 297.073617
## iter 320 value 295.519161
## iter 330 value 294.576755
## iter 340 value 294.161286
## iter 350 value 294.007460
## iter 360 value 293.947444
## iter 370 value 293.680345
## iter 380 value 292.722926
## iter 390 value 291.119413
## iter 400 value 290.113679
## iter 410 value 289.913108
## iter 420 value 289.873115
## iter 430 value 289.867138
## iter 440 value 289.866708
## iter 450 value 289.865698
## iter 460 value 289.863793
## iter 470 value 289.860844
## iter 480 value 289.858005
## iter 490 value 289.855406
## iter 500 value 289.853558
## final  value 289.853558 
## stopped after 500 iterations
## # weights:  141
## initial  value 1397917.031843 
## iter  10 value 1359.837703
## iter  20 value 1056.886160
## iter  30 value 888.798096
## iter  40 value 816.433030
## iter  50 value 720.584238
## iter  60 value 617.674906
## iter  70 value 524.957506
## iter  80 value 486.309386
## iter  90 value 444.959992
## iter 100 value 421.098888
## iter 110 value 386.121525
## iter 120 value 351.675891
## iter 130 value 336.895899
## iter 140 value 323.907115
## iter 150 value 308.911057
## iter 160 value 290.344204
## iter 170 value 277.100566
## iter 180 value 268.845961
## iter 190 value 261.411070
## iter 200 value 254.461326
## iter 210 value 245.373395
## iter 220 value 237.259665
## iter 230 value 231.892659
## iter 240 value 228.379697
## iter 250 value 225.165446
## iter 260 value 222.501844
## iter 270 value 219.469101
## iter 280 value 217.870288
## iter 290 value 217.446960
## iter 300 value 217.110444
## iter 310 value 216.729673
## iter 320 value 216.115111
## iter 330 value 215.349188
## iter 340 value 214.093226
## iter 350 value 212.505758
## iter 360 value 211.019023
## iter 370 value 209.027055
## iter 380 value 207.599435
## iter 390 value 205.898556
## iter 400 value 204.266096
## iter 410 value 202.879831
## iter 420 value 201.225020
## iter 430 value 199.766989
## iter 440 value 197.682816
## iter 450 value 196.172051
## iter 460 value 194.850052
## iter 470 value 193.808291
## iter 480 value 193.203684
## iter 490 value 192.699709
## iter 500 value 192.429809
## final  value 192.429809 
## stopped after 500 iterations
## # weights:  15
## initial  value 1418647.988848 
## iter  10 value 7802.726941
## iter  20 value 3190.461116
## iter  30 value 1701.366337
## iter  40 value 1555.977145
## iter  50 value 1471.532427
## iter  60 value 1404.936526
## iter  70 value 1201.489434
## iter  80 value 1140.062653
## iter  90 value 1134.717964
## iter 100 value 1124.395355
## iter 110 value 1121.952263
## iter 120 value 1121.358112
## iter 130 value 1119.606285
## iter 140 value 1118.254126
## iter 150 value 1118.197345
## iter 160 value 1117.807172
## iter 170 value 1116.931237
## iter 180 value 1116.921685
## iter 190 value 1116.867487
## iter 200 value 1116.533878
## iter 210 value 1116.510917
## iter 220 value 1116.493598
## iter 230 value 1116.446357
## final  value 1116.445325 
## converged
## # weights:  36
## initial  value 1427170.219825 
## iter  10 value 39071.045249
## iter  20 value 9279.542911
## iter  30 value 2874.803980
## iter  40 value 1736.277200
## iter  50 value 1497.026126
## iter  60 value 1373.409921
## iter  70 value 1339.471009
## iter  80 value 1313.762026
## iter  90 value 1261.645624
## iter 100 value 1250.490423
## iter 110 value 1245.566963
## iter 120 value 1241.579924
## iter 130 value 1239.196521
## iter 140 value 1217.123366
## iter 150 value 1174.713316
## iter 160 value 1167.636967
## iter 170 value 1161.038392
## iter 180 value 1156.729875
## iter 190 value 1155.155791
## iter 200 value 1149.459696
## iter 210 value 1148.276345
## iter 220 value 1145.858745
## iter 230 value 1145.453377
## iter 240 value 1144.060618
## iter 250 value 1143.535749
## iter 260 value 1143.404843
## iter 270 value 1141.279029
## iter 280 value 1121.017612
## iter 290 value 1087.147054
## iter 300 value 1066.275535
## iter 310 value 1055.023777
## iter 320 value 1049.716266
## iter 330 value 1046.276794
## iter 340 value 1042.206722
## iter 350 value 1031.494478
## iter 360 value 1025.534778
## iter 370 value 1021.870643
## iter 380 value 1019.831706
## iter 390 value 1018.671908
## iter 400 value 1018.144157
## iter 410 value 1017.944022
## iter 420 value 1017.693434
## iter 430 value 1017.633236
## iter 440 value 1017.506815
## iter 450 value 1017.309112
## iter 460 value 1017.286308
## iter 470 value 1017.268432
## iter 470 value 1017.268426
## final  value 1017.268426 
## converged
## # weights:  71
## initial  value 1426719.876281 
## iter  10 value 1783.190871
## iter  20 value 1114.935635
## iter  30 value 992.551670
## iter  40 value 917.560367
## iter  50 value 857.733813
## iter  60 value 821.912889
## iter  70 value 800.687860
## iter  80 value 789.036687
## iter  90 value 782.639580
## iter 100 value 770.919928
## iter 110 value 764.131194
## iter 120 value 758.322088
## iter 130 value 755.538800
## iter 140 value 754.511775
## iter 150 value 753.983869
## iter 160 value 753.516688
## iter 170 value 752.056473
## iter 180 value 748.354181
## iter 190 value 746.074462
## iter 200 value 740.436190
## iter 210 value 729.813844
## iter 220 value 723.273467
## iter 230 value 719.326572
## iter 240 value 710.953646
## iter 250 value 704.119830
## iter 260 value 698.136399
## iter 270 value 695.641047
## iter 280 value 692.206845
## iter 290 value 689.423838
## iter 300 value 688.609514
## iter 310 value 687.874940
## iter 320 value 686.510198
## iter 330 value 686.074217
## iter 340 value 685.927384
## iter 350 value 685.658303
## iter 360 value 685.486237
## iter 370 value 684.585305
## iter 380 value 682.903028
## iter 390 value 682.160421
## iter 400 value 681.188697
## iter 410 value 680.507383
## iter 420 value 680.214694
## iter 430 value 680.185311
## iter 440 value 680.061735
## iter 450 value 680.041336
## iter 460 value 680.034540
## iter 460 value 680.034534
## iter 460 value 680.034532
## final  value 680.034532 
## converged
## # weights:  106
## initial  value 1381374.794335 
## iter  10 value 1299.734265
## iter  20 value 1056.887523
## iter  30 value 924.246473
## iter  40 value 820.543008
## iter  50 value 771.663785
## iter  60 value 708.455744
## iter  70 value 663.519686
## iter  80 value 636.717381
## iter  90 value 608.681548
## iter 100 value 597.299079
## iter 110 value 574.245431
## iter 120 value 537.280028
## iter 130 value 512.425534
## iter 140 value 490.554618
## iter 150 value 462.802398
## iter 160 value 442.416228
## iter 170 value 422.548174
## iter 180 value 411.453751
## iter 190 value 400.732569
## iter 200 value 393.325163
## iter 210 value 388.684376
## iter 220 value 387.294850
## iter 230 value 385.792397
## iter 240 value 383.828614
## iter 250 value 379.613521
## iter 260 value 375.572724
## iter 270 value 367.784379
## iter 280 value 358.778228
## iter 290 value 352.815450
## iter 300 value 348.352702
## iter 310 value 341.713739
## iter 320 value 337.311272
## iter 330 value 333.400939
## iter 340 value 329.210252
## iter 350 value 326.817503
## iter 360 value 324.349044
## iter 370 value 322.528903
## iter 380 value 312.804611
## iter 390 value 309.146648
## iter 400 value 305.206635
## iter 410 value 303.031465
## iter 420 value 301.705990
## iter 430 value 301.186973
## iter 440 value 301.102088
## iter 450 value 300.964212
## iter 460 value 300.765090
## iter 470 value 300.643788
## iter 480 value 300.526312
## iter 490 value 300.414483
## iter 500 value 300.309006
## final  value 300.309006 
## stopped after 500 iterations
## # weights:  141
## initial  value 1348649.262086 
## iter  10 value 1797.641927
## iter  20 value 1103.834976
## iter  30 value 959.107228
## iter  40 value 863.552441
## iter  50 value 777.611731
## iter  60 value 688.022225
## iter  70 value 600.893262
## iter  80 value 546.384911
## iter  90 value 493.258586
## iter 100 value 445.222399
## iter 110 value 416.494274
## iter 120 value 397.464881
## iter 130 value 385.392480
## iter 140 value 371.794476
## iter 150 value 355.871300
## iter 160 value 334.104825
## iter 170 value 311.003225
## iter 180 value 298.266561
## iter 190 value 288.192667
## iter 200 value 279.541700
## iter 210 value 270.401998
## iter 220 value 262.201877
## iter 230 value 251.713556
## iter 240 value 246.065889
## iter 250 value 241.961546
## iter 260 value 239.441350
## iter 270 value 237.927797
## iter 280 value 236.833032
## iter 290 value 236.402665
## iter 300 value 235.956323
## iter 310 value 234.896134
## iter 320 value 233.127371
## iter 330 value 231.783961
## iter 340 value 229.847097
## iter 350 value 226.199324
## iter 360 value 223.951083
## iter 370 value 221.235639
## iter 380 value 218.266239
## iter 390 value 215.617201
## iter 400 value 214.288562
## iter 410 value 212.057061
## iter 420 value 210.885106
## iter 430 value 208.335861
## iter 440 value 206.301861
## iter 450 value 205.231426
## iter 460 value 204.470067
## iter 470 value 203.790337
## iter 480 value 203.617950
## iter 490 value 203.526646
## iter 500 value 203.453954
## final  value 203.453954 
## stopped after 500 iterations
## # weights:  15
## initial  value 1437213.302387 
## iter  10 value 5255.207331
## iter  20 value 3949.388728
## iter  30 value 2290.690213
## iter  40 value 1836.722757
## iter  50 value 1770.770430
## iter  60 value 1751.021590
## iter  70 value 1739.954030
## iter  80 value 1736.133683
## iter  90 value 1735.518402
## iter  90 value 1735.518397
## final  value 1735.517841 
## converged
## # weights:  36
## initial  value 1403685.143634 
## iter  10 value 7291.596634
## iter  20 value 3345.667307
## iter  30 value 2854.230526
## iter  40 value 2735.652249
## iter  50 value 2662.486507
## iter  60 value 2568.819529
## iter  70 value 2219.920956
## iter  80 value 1980.937463
## iter  90 value 1679.683030
## iter 100 value 1563.678027
## iter 110 value 1545.352535
## iter 120 value 1526.578521
## iter 130 value 1515.343169
## iter 140 value 1506.192877
## iter 150 value 1498.975117
## iter 160 value 1490.369460
## iter 170 value 1487.801718
## iter 180 value 1485.897615
## iter 190 value 1485.775181
## iter 200 value 1485.650531
## iter 210 value 1485.501996
## iter 220 value 1485.497904
## iter 230 value 1485.484835
## final  value 1485.484739 
## converged
## # weights:  71
## initial  value 1371779.205282 
## iter  10 value 2041.259453
## iter  20 value 1180.730791
## iter  30 value 965.307145
## iter  40 value 896.110973
## iter  50 value 853.485688
## iter  60 value 830.740975
## iter  70 value 786.858172
## iter  80 value 758.653251
## iter  90 value 736.067309
## iter 100 value 696.525942
## iter 110 value 682.682772
## iter 120 value 668.194406
## iter 130 value 658.446248
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## iter 160 value 644.940752
## iter 170 value 639.861777
## iter 180 value 631.223875
## iter 190 value 620.480615
## iter 200 value 609.807272
## iter 210 value 592.550789
## iter 220 value 577.476706
## iter 230 value 562.064976
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## iter 250 value 551.388826
## iter 260 value 550.354304
## iter 270 value 547.266167
## iter 280 value 543.700049
## iter 290 value 541.048983
## iter 300 value 540.807648
## iter 310 value 539.926757
## iter 320 value 539.513071
## iter 330 value 539.051649
## iter 340 value 537.980287
## iter 350 value 536.659244
## iter 360 value 534.851035
## iter 370 value 533.427602
## iter 380 value 532.381938
## iter 390 value 531.378447
## iter 400 value 531.114151
## iter 410 value 530.845823
## iter 420 value 529.984520
## iter 430 value 529.499983
## iter 440 value 529.442417
## iter 450 value 529.390786
## iter 460 value 529.339436
## iter 470 value 529.313635
## iter 480 value 529.283218
## iter 490 value 529.256925
## iter 500 value 529.176818
## final  value 529.176818 
## stopped after 500 iterations
## # weights:  106
## initial  value 1389419.598289 
## iter  10 value 1864.740434
## iter  20 value 1103.814917
## iter  30 value 899.655533
## iter  40 value 826.906983
## iter  50 value 782.069824
## iter  60 value 709.767414
## iter  70 value 652.485898
## iter  80 value 609.957881
## iter  90 value 556.684149
## iter 100 value 521.057830
## iter 110 value 495.432430
## iter 120 value 478.974511
## iter 130 value 452.402426
## iter 140 value 432.842799
## iter 150 value 421.762568
## iter 160 value 416.535023
## iter 170 value 412.628814
## iter 180 value 404.223314
## iter 190 value 390.251542
## iter 200 value 377.633514
## iter 210 value 365.780165
## iter 220 value 360.538325
## iter 230 value 358.682043
## iter 240 value 355.502192
## iter 250 value 352.300711
## iter 260 value 349.395695
## iter 270 value 345.029110
## iter 280 value 337.899434
## iter 290 value 334.029557
## iter 300 value 330.642420
## iter 310 value 325.184476
## iter 320 value 322.187385
## iter 330 value 319.863822
## iter 340 value 318.644723
## iter 350 value 316.897860
## iter 360 value 316.045842
## iter 370 value 314.304491
## iter 380 value 313.551632
## iter 390 value 312.206192
## iter 400 value 310.259822
## iter 410 value 309.724034
## iter 420 value 309.610816
## iter 430 value 309.584429
## iter 440 value 309.582305
## iter 450 value 309.576328
## iter 460 value 309.569959
## iter 470 value 309.565284
## iter 480 value 309.563138
## iter 490 value 309.560861
## iter 500 value 309.554007
## final  value 309.554007 
## stopped after 500 iterations
## # weights:  141
## initial  value 1398016.686222 
## iter  10 value 1565.105950
## iter  20 value 1099.277338
## iter  30 value 899.889456
## iter  40 value 778.738743
## iter  50 value 699.333858
## iter  60 value 622.568080
## iter  70 value 534.120331
## iter  80 value 475.671982
## iter  90 value 444.124394
## iter 100 value 420.785442
## iter 110 value 404.554371
## iter 120 value 380.880265
## iter 130 value 356.425700
## iter 140 value 338.182778
## iter 150 value 321.201687
## iter 160 value 312.942894
## iter 170 value 297.639043
## iter 180 value 278.335166
## iter 190 value 267.868917
## iter 200 value 259.408266
## iter 210 value 253.633228
## iter 220 value 235.519085
## iter 230 value 229.508877
## iter 240 value 225.124058
## iter 250 value 218.562371
## iter 260 value 212.662072
## iter 270 value 207.902502
## iter 280 value 201.673356
## iter 290 value 199.623885
## iter 300 value 198.491262
## iter 310 value 195.968982
## iter 320 value 193.987561
## iter 330 value 191.419691
## iter 340 value 189.416765
## iter 350 value 187.937937
## iter 360 value 186.007531
## iter 370 value 182.269374
## iter 380 value 179.049133
## iter 390 value 173.622423
## iter 400 value 171.299692
## iter 410 value 168.012696
## iter 420 value 164.550174
## iter 430 value 162.301052
## iter 440 value 161.243032
## iter 450 value 160.126985
## iter 460 value 158.845396
## iter 470 value 157.202095
## iter 480 value 155.260796
## iter 490 value 153.961776
## iter 500 value 152.949283
## final  value 152.949283 
## stopped after 500 iterations
## # weights:  15
## initial  value 1427199.364592 
## iter  10 value 3242.688199
## iter  20 value 2249.407741
## iter  30 value 1595.837381
## iter  40 value 1557.964498
## iter  50 value 1491.031424
## iter  60 value 1457.539530
## iter  70 value 1448.962899
## iter  80 value 1442.410842
## iter  90 value 1426.997669
## iter 100 value 1421.633111
## iter 110 value 1420.373954
## iter 120 value 1418.315760
## iter 130 value 1416.125748
## iter 140 value 1415.880539
## iter 150 value 1413.615922
## iter 160 value 1412.632274
## iter 170 value 1412.410624
## iter 180 value 1411.948765
## iter 190 value 1410.971972
## iter 200 value 1410.942898
## iter 210 value 1410.518671
## iter 220 value 1409.852664
## iter 230 value 1409.831473
## iter 240 value 1409.618982
## iter 250 value 1409.483457
## iter 260 value 1409.461759
## iter 270 value 1409.152783
## iter 280 value 1409.043085
## iter 290 value 1408.993616
## iter 300 value 1408.822682
## iter 310 value 1408.784378
## iter 320 value 1408.776997
## iter 330 value 1408.658837
## iter 340 value 1408.571740
## iter 350 value 1408.546493
## iter 360 value 1408.465372
## iter 370 value 1408.393801
## final  value 1408.382644 
## converged
## # weights:  36
## initial  value 1387443.119112 
## iter  10 value 5029.065347
## iter  20 value 2912.972548
## iter  30 value 2237.542984
## iter  40 value 2006.347229
## iter  50 value 1909.375173
## iter  60 value 1790.946282
## iter  70 value 1712.613145
## iter  80 value 1681.725961
## iter  90 value 1599.311611
## iter 100 value 1516.571936
## iter 110 value 1422.507640
## iter 120 value 1346.716619
## iter 130 value 1291.638892
## iter 140 value 1271.103397
## iter 150 value 1258.578434
## iter 160 value 1253.209060
## iter 170 value 1248.402212
## iter 180 value 1245.130798
## iter 190 value 1243.481291
## iter 200 value 1241.810653
## iter 210 value 1239.466220
## iter 220 value 1238.353674
## iter 230 value 1237.834079
## iter 240 value 1237.643670
## iter 240 value 1237.643663
## final  value 1237.643421 
## converged
## # weights:  71
## initial  value 1360784.265008 
## iter  10 value 2446.221325
## iter  20 value 1437.744290
## iter  30 value 1185.414210
## iter  40 value 1088.668630
## iter  50 value 1052.982109
## iter  60 value 1005.474316
## iter  70 value 986.691240
## iter  80 value 959.294830
## iter  90 value 903.219676
## iter 100 value 859.172852
## iter 110 value 836.883717
## iter 120 value 809.661391
## iter 130 value 799.897828
## iter 140 value 790.158781
## iter 150 value 780.159581
## iter 160 value 775.703920
## iter 170 value 767.937968
## iter 180 value 759.076911
## iter 190 value 744.712439
## iter 200 value 726.770310
## iter 210 value 699.079043
## iter 220 value 682.734124
## iter 230 value 672.577403
## iter 240 value 666.041811
## iter 250 value 658.411027
## iter 260 value 655.067148
## iter 270 value 652.181160
## iter 280 value 650.666221
## iter 290 value 650.035753
## iter 300 value 649.809694
## iter 310 value 649.175119
## iter 320 value 648.216135
## iter 330 value 647.233034
## iter 340 value 645.887567
## iter 350 value 645.033747
## iter 360 value 644.218599
## iter 370 value 643.494702
## iter 380 value 641.894373
## iter 390 value 630.928016
## iter 400 value 618.027459
## iter 410 value 611.767345
## iter 420 value 605.466233
## iter 430 value 601.173379
## iter 440 value 600.948404
## iter 450 value 600.047340
## iter 460 value 599.613169
## iter 470 value 597.130243
## iter 480 value 592.026027
## iter 490 value 584.257597
## iter 500 value 574.192437
## final  value 574.192437 
## stopped after 500 iterations
## # weights:  106
## initial  value 1366264.493128 
## iter  10 value 1797.543543
## iter  20 value 1106.069376
## iter  30 value 974.949719
## iter  40 value 881.972848
## iter  50 value 793.602851
## iter  60 value 746.691279
## iter  70 value 696.980319
## iter  80 value 643.653277
## iter  90 value 597.506944
## iter 100 value 566.416881
## iter 110 value 535.680188
## iter 120 value 495.313494
## iter 130 value 481.670133
## iter 140 value 462.008820
## iter 150 value 442.253220
## iter 160 value 421.758974
## iter 170 value 408.823868
## iter 180 value 394.950045
## iter 190 value 386.101005
## iter 200 value 372.902567
## iter 210 value 367.537189
## iter 220 value 365.113668
## iter 230 value 363.852430
## iter 240 value 362.057798
## iter 250 value 359.882270
## iter 260 value 357.754747
## iter 270 value 354.472726
## iter 280 value 350.999064
## iter 290 value 345.225527
## iter 300 value 335.930593
## iter 310 value 327.823448
## iter 320 value 322.815772
## iter 330 value 320.863011
## iter 340 value 319.408929
## iter 350 value 318.297116
## iter 360 value 317.594408
## iter 370 value 317.177555
## iter 380 value 316.666621
## iter 390 value 315.990469
## iter 400 value 315.371832
## iter 410 value 314.658312
## iter 420 value 313.700616
## iter 430 value 312.902312
## iter 440 value 312.864354
## iter 450 value 312.796909
## iter 460 value 312.631537
## iter 470 value 312.441780
## iter 480 value 312.224011
## iter 490 value 311.993086
## iter 500 value 311.869517
## final  value 311.869517 
## stopped after 500 iterations
## # weights:  141
## initial  value 1372533.323581 
## iter  10 value 1480.367520
## iter  20 value 1126.726901
## iter  30 value 988.676507
## iter  40 value 840.096714
## iter  50 value 762.684788
## iter  60 value 664.892252
## iter  70 value 633.842942
## iter  80 value 594.420480
## iter  90 value 564.531276
## iter 100 value 525.018027
## iter 110 value 501.725913
## iter 120 value 481.878288
## iter 130 value 467.392250
## iter 140 value 448.444260
## iter 150 value 437.807810
## iter 160 value 423.531165
## iter 170 value 406.767804
## iter 180 value 381.567118
## iter 190 value 363.032250
## iter 200 value 348.566988
## iter 210 value 335.391371
## iter 220 value 322.134637
## iter 230 value 312.566423
## iter 240 value 285.064343
## iter 250 value 256.470562
## iter 260 value 244.150241
## iter 270 value 239.503331
## iter 280 value 236.672330
## iter 290 value 235.262732
## iter 300 value 234.355997
## iter 310 value 232.702594
## iter 320 value 230.556446
## iter 330 value 226.494332
## iter 340 value 222.311119
## iter 350 value 219.220469
## iter 360 value 217.238618
## iter 370 value 213.503818
## iter 380 value 205.421389
## iter 390 value 200.221458
## iter 400 value 197.021718
## iter 410 value 192.395241
## iter 420 value 184.411012
## iter 430 value 180.493921
## iter 440 value 178.251529
## iter 450 value 177.501438
## iter 460 value 177.168799
## iter 470 value 176.316968
## iter 480 value 174.794190
## iter 490 value 173.118031
## iter 500 value 171.821966
## final  value 171.821966 
## stopped after 500 iterations
## # weights:  15
## initial  value 1400219.291998 
## iter  10 value 16325.030064
## iter  20 value 15066.481578
## iter  30 value 12981.858867
## iter  40 value 4474.582093
## iter  50 value 2971.566720
## iter  60 value 2575.484846
## iter  70 value 2028.998508
## iter  80 value 1925.122545
## iter  90 value 1923.681091
## final  value 1923.234613 
## converged
## # weights:  36
## initial  value 1431727.198108 
## iter  10 value 8284.986412
## iter  20 value 5295.495445
## iter  30 value 4118.975282
## iter  40 value 2633.104348
## iter  50 value 1940.944050
## iter  60 value 1679.685953
## iter  70 value 1598.626483
## iter  80 value 1507.322957
## iter  90 value 1438.811629
## iter 100 value 1419.836307
## iter 110 value 1376.251878
## iter 120 value 1325.691046
## iter 130 value 1289.892434
## iter 140 value 1281.453328
## iter 150 value 1278.971264
## iter 160 value 1278.561145
## final  value 1278.560941 
## converged
## # weights:  71
## initial  value 1379117.849918 
## iter  10 value 2989.077526
## iter  20 value 1955.114958
## iter  30 value 1633.446156
## iter  40 value 1424.928873
## iter  50 value 1370.391419
## iter  60 value 1311.298776
## iter  70 value 1275.096579
## iter  80 value 1227.828832
## iter  90 value 1178.794754
## iter 100 value 1138.618479
## iter 110 value 1115.821019
## iter 120 value 1092.705933
## iter 130 value 1039.559949
## iter 140 value 1003.059109
## iter 150 value 991.834398
## iter 160 value 983.513802
## iter 170 value 975.648985
## iter 180 value 970.955511
## iter 190 value 967.587718
## iter 200 value 955.113470
## iter 210 value 938.152614
## iter 220 value 923.314055
## iter 230 value 919.603360
## iter 240 value 918.475428
## iter 250 value 918.446634
## iter 260 value 918.443057
## final  value 918.442995 
## converged
## # weights:  106
## initial  value 1410758.266674 
## iter  10 value 2535.453940
## iter  20 value 1505.396895
## iter  30 value 1210.490180
## iter  40 value 1105.520629
## iter  50 value 1011.228998
## iter  60 value 933.757222
## iter  70 value 903.586133
## iter  80 value 891.623376
## iter  90 value 878.391404
## iter 100 value 863.905128
## iter 110 value 852.119581
## iter 120 value 836.922741
## iter 130 value 822.923312
## iter 140 value 805.659807
## iter 150 value 793.258493
## iter 160 value 782.291004
## iter 170 value 775.937624
## iter 180 value 767.753979
## iter 190 value 762.703005
## iter 200 value 749.728469
## iter 210 value 744.772040
## iter 220 value 740.534786
## iter 230 value 735.307256
## iter 240 value 729.975623
## iter 250 value 726.056542
## iter 260 value 721.853876
## iter 270 value 717.210090
## iter 280 value 710.256634
## iter 290 value 706.554727
## iter 300 value 703.789616
## iter 310 value 702.364107
## iter 320 value 700.224333
## iter 330 value 699.050215
## iter 340 value 697.660589
## iter 350 value 694.039316
## iter 360 value 692.655964
## iter 370 value 692.407043
## iter 380 value 692.306624
## iter 390 value 692.279008
## iter 400 value 692.266105
## iter 410 value 692.260321
## final  value 692.259869 
## converged
## # weights:  141
## initial  value 1391336.697576 
## iter  10 value 1679.134965
## iter  20 value 1160.969209
## iter  30 value 1012.610725
## iter  40 value 906.566783
## iter  50 value 838.487991
## iter  60 value 797.421465
## iter  70 value 764.611789
## iter  80 value 715.557849
## iter  90 value 692.928984
## iter 100 value 680.303625
## iter 110 value 674.730589
## iter 120 value 665.497172
## iter 130 value 645.807107
## iter 140 value 626.233835
## iter 150 value 615.015905
## iter 160 value 608.383590
## iter 170 value 603.948516
## iter 180 value 599.559590
## iter 190 value 595.078801
## iter 200 value 587.977366
## iter 210 value 581.186089
## iter 220 value 572.420636
## iter 230 value 569.002586
## iter 240 value 567.395920
## iter 250 value 566.056267
## iter 260 value 565.253453
## iter 270 value 564.491671
## iter 280 value 563.705579
## iter 290 value 563.256366
## iter 300 value 562.719545
## iter 310 value 561.778606
## iter 320 value 561.312043
## iter 330 value 561.071028
## iter 340 value 560.867456
## iter 350 value 560.582783
## iter 360 value 560.216370
## iter 370 value 559.260623
## iter 380 value 557.929919
## iter 390 value 556.965646
## iter 400 value 556.441930
## iter 410 value 555.893961
## iter 420 value 554.017782
## iter 430 value 551.394580
## iter 440 value 548.713322
## iter 450 value 547.269798
## iter 460 value 546.723920
## iter 470 value 546.286213
## iter 480 value 546.238980
## iter 490 value 546.228318
## iter 500 value 546.227711
## final  value 546.227711 
## stopped after 500 iterations
## # weights:  15
## initial  value 1404826.362613 
## iter  10 value 32721.877973
## iter  20 value 6453.845876
## iter  30 value 5737.494193
## iter  40 value 4633.977981
## iter  50 value 3619.024104
## iter  60 value 2526.020037
## iter  70 value 1808.841350
## iter  80 value 1556.222365
## iter  90 value 1497.069115
## iter 100 value 1484.569417
## iter 110 value 1469.349787
## iter 120 value 1461.292695
## iter 130 value 1459.511442
## iter 140 value 1459.149414
## iter 150 value 1457.858074
## iter 160 value 1457.761467
## iter 170 value 1457.733183
## iter 180 value 1457.673060
## final  value 1457.659457 
## converged
## # weights:  36
## initial  value 1444227.392543 
## iter  10 value 5062.236456
## iter  20 value 4675.618592
## iter  30 value 3435.776310
## iter  40 value 2876.615324
## iter  50 value 1721.785709
## iter  60 value 1462.511986
## iter  70 value 1384.303676
## iter  80 value 1325.151246
## iter  90 value 1261.684157
## iter 100 value 1251.574189
## iter 110 value 1219.100979
## iter 120 value 1161.555827
## iter 130 value 1082.304591
## iter 140 value 1020.980104
## iter 150 value 980.983478
## iter 160 value 957.879852
## iter 170 value 950.836718
## iter 180 value 948.142159
## iter 190 value 940.976788
## iter 200 value 939.305717
## iter 210 value 927.920022
## iter 220 value 901.157306
## iter 230 value 895.205046
## iter 240 value 892.141878
## iter 250 value 891.946879
## iter 260 value 891.754949
## iter 270 value 891.748145
## iter 270 value 891.748140
## final  value 891.748116 
## converged
## # weights:  71
## initial  value 1429826.480921 
## iter  10 value 3908.022721
## iter  20 value 1904.190811
## iter  30 value 1511.543426
## iter  40 value 1215.663584
## iter  50 value 1015.831856
## iter  60 value 952.940472
## iter  70 value 925.690631
## iter  80 value 910.993328
## iter  90 value 887.954082
## iter 100 value 865.824288
## iter 110 value 846.571250
## iter 120 value 833.556379
## iter 130 value 825.822751
## iter 140 value 816.510487
## iter 150 value 806.228886
## iter 160 value 800.818405
## iter 170 value 792.894778
## iter 180 value 787.195638
## iter 190 value 784.054576
## iter 200 value 780.978011
## iter 210 value 778.992896
## iter 220 value 777.771203
## iter 230 value 777.018584
## iter 240 value 776.140884
## iter 250 value 776.005139
## iter 260 value 774.062682
## iter 270 value 771.044483
## iter 280 value 761.139693
## iter 290 value 753.227927
## iter 300 value 741.255093
## iter 310 value 734.128351
## iter 320 value 728.859687
## iter 330 value 727.273290
## iter 340 value 726.289155
## iter 350 value 726.033905
## iter 360 value 725.606873
## iter 370 value 725.099174
## iter 380 value 723.368673
## iter 390 value 722.471860
## iter 400 value 722.172141
## iter 410 value 721.683755
## iter 420 value 720.940501
## iter 430 value 719.776472
## iter 440 value 718.354904
## iter 450 value 715.993559
## iter 460 value 714.307419
## iter 470 value 711.366624
## iter 480 value 704.823333
## iter 490 value 701.187206
## iter 500 value 699.438202
## final  value 699.438202 
## stopped after 500 iterations
## # weights:  106
## initial  value 1395679.369946 
## iter  10 value 1497.985843
## iter  20 value 1108.336926
## iter  30 value 951.659517
## iter  40 value 851.905880
## iter  50 value 768.214187
## iter  60 value 691.017469
## iter  70 value 633.929690
## iter  80 value 608.907073
## iter  90 value 582.681975
## iter 100 value 556.747459
## iter 110 value 536.767086
## iter 120 value 524.995917
## iter 130 value 514.789354
## iter 140 value 504.574917
## iter 150 value 487.104922
## iter 160 value 476.344342
## iter 170 value 465.535386
## iter 180 value 455.164252
## iter 190 value 445.192897
## iter 200 value 441.116022
## iter 210 value 439.150846
## iter 220 value 437.836622
## iter 230 value 437.171045
## iter 240 value 434.934696
## iter 250 value 432.346010
## iter 260 value 429.801991
## iter 270 value 427.981366
## iter 280 value 419.640661
## iter 290 value 403.940257
## iter 300 value 395.876854
## iter 310 value 386.488723
## iter 320 value 381.275499
## iter 330 value 377.688796
## iter 340 value 371.857174
## iter 350 value 366.901524
## iter 360 value 360.397980
## iter 370 value 355.648910
## iter 380 value 354.022210
## iter 390 value 353.280329
## iter 400 value 353.023956
## iter 410 value 352.872002
## iter 420 value 352.743042
## iter 430 value 352.657504
## iter 440 value 352.649331
## iter 450 value 352.634584
## iter 460 value 352.597679
## iter 470 value 352.529372
## iter 480 value 352.464494
## iter 490 value 352.423608
## iter 500 value 352.390627
## final  value 352.390627 
## stopped after 500 iterations
## # weights:  141
## initial  value 1402396.474510 
## iter  10 value 1565.384025
## iter  20 value 1095.466559
## iter  30 value 911.356603
## iter  40 value 812.853393
## iter  50 value 732.232344
## iter  60 value 665.504817
## iter  70 value 601.970845
## iter  80 value 527.318422
## iter  90 value 471.846724
## iter 100 value 435.855865
## iter 110 value 402.716331
## iter 120 value 368.427592
## iter 130 value 339.663207
## iter 140 value 318.397535
## iter 150 value 308.567002
## iter 160 value 298.716965
## iter 170 value 289.773355
## iter 180 value 279.397730
## iter 190 value 270.094790
## iter 200 value 262.558848
## iter 210 value 256.933785
## iter 220 value 248.609932
## iter 230 value 240.671532
## iter 240 value 235.995528
## iter 250 value 231.408183
## iter 260 value 229.583538
## iter 270 value 224.923008
## iter 280 value 221.256600
## iter 290 value 220.228144
## iter 300 value 219.521360
## iter 310 value 218.156805
## iter 320 value 214.315702
## iter 330 value 210.610226
## iter 340 value 208.201640
## iter 350 value 206.429068
## iter 360 value 205.146683
## iter 370 value 204.094262
## iter 380 value 203.192205
## iter 390 value 201.312009
## iter 400 value 196.900440
## iter 410 value 195.181317
## iter 420 value 193.423232
## iter 430 value 192.595965
## iter 440 value 191.757948
## iter 450 value 191.145245
## iter 460 value 190.919524
## iter 470 value 190.849527
## iter 480 value 190.817376
## iter 490 value 190.797801
## iter 500 value 190.786654
## final  value 190.786654 
## stopped after 500 iterations
## # weights:  15
## initial  value 1451473.195072 
## iter  10 value 9999.874955
## iter  20 value 6284.155426
## iter  30 value 3408.293275
## iter  40 value 2346.660738
## iter  50 value 1861.534597
## iter  60 value 1804.594349
## iter  70 value 1797.358378
## iter  80 value 1787.590490
## iter  90 value 1779.595845
## iter 100 value 1764.417013
## iter 110 value 1742.360651
## iter 120 value 1680.786733
## iter 130 value 1581.952025
## iter 140 value 1560.219304
## iter 150 value 1532.758315
## iter 160 value 1526.297509
## iter 170 value 1525.765720
## iter 180 value 1524.909073
## iter 190 value 1523.339970
## iter 200 value 1523.247658
## final  value 1523.247106 
## converged
## # weights:  36
## initial  value 1426398.049107 
## iter  10 value 48432.755975
## iter  20 value 6519.976709
## iter  30 value 5381.745208
## iter  40 value 3391.785638
## iter  50 value 2359.933558
## iter  60 value 1951.955342
## iter  70 value 1638.893474
## iter  80 value 1544.165259
## iter  90 value 1368.390150
## iter 100 value 1205.954665
## iter 110 value 1153.346121
## iter 120 value 1105.196885
## iter 130 value 1079.219723
## iter 140 value 1060.185847
## iter 150 value 1057.518592
## iter 160 value 1054.773903
## iter 170 value 1044.352646
## iter 180 value 1027.110759
## iter 190 value 1018.048185
## iter 200 value 1013.520825
## iter 210 value 1009.233565
## iter 220 value 1006.930774
## iter 230 value 1006.877489
## iter 240 value 1005.724700
## iter 250 value 1004.701416
## iter 260 value 1002.664993
## iter 270 value 1001.475816
## iter 280 value 1001.002681
## iter 290 value 994.731485
## iter 300 value 993.200207
## iter 310 value 992.949445
## iter 320 value 992.477898
## iter 330 value 991.727619
## iter 340 value 991.567608
## iter 350 value 991.543638
## iter 360 value 991.414729
## iter 370 value 991.313927
## iter 380 value 990.812654
## iter 390 value 990.350406
## iter 400 value 990.296293
## iter 410 value 990.221590
## iter 420 value 990.212721
## iter 430 value 990.195420
## iter 440 value 990.189898
## iter 450 value 990.165524
## iter 460 value 990.126153
## iter 470 value 990.123638
## final  value 990.118601 
## converged
## # weights:  71
## initial  value 1405972.882724 
## iter  10 value 1915.326261
## iter  20 value 1194.196347
## iter  30 value 1118.476386
## iter  40 value 985.901710
## iter  50 value 919.250758
## iter  60 value 893.044280
## iter  70 value 881.790577
## iter  80 value 860.052497
## iter  90 value 809.760319
## iter 100 value 774.911884
## iter 110 value 755.643078
## iter 120 value 732.841669
## iter 130 value 719.510505
## iter 140 value 701.017700
## iter 150 value 692.489865
## iter 160 value 688.667336
## iter 170 value 682.649356
## iter 180 value 676.086047
## iter 190 value 671.327534
## iter 200 value 660.854934
## iter 210 value 653.604903
## iter 220 value 639.407987
## iter 230 value 636.521749
## iter 240 value 635.594748
## iter 250 value 635.178167
## iter 260 value 634.368150
## iter 270 value 633.777238
## iter 280 value 633.701321
## iter 290 value 633.526592
## iter 300 value 633.501663
## iter 310 value 633.466699
## iter 320 value 633.402470
## iter 330 value 632.969770
## iter 340 value 627.240871
## iter 350 value 608.010389
## iter 360 value 599.090730
## iter 370 value 597.674555
## iter 380 value 597.485534
## iter 390 value 597.215531
## iter 400 value 596.999744
## iter 410 value 596.658741
## iter 420 value 596.607103
## iter 430 value 596.534104
## iter 440 value 596.440549
## iter 450 value 596.013803
## iter 460 value 595.929490
## iter 470 value 595.849942
## iter 480 value 595.785580
## iter 490 value 595.736454
## iter 500 value 595.700857
## final  value 595.700857 
## stopped after 500 iterations
## # weights:  106
## initial  value 1418359.832409 
## iter  10 value 1452.786704
## iter  20 value 1036.847032
## iter  30 value 920.106690
## iter  40 value 833.317077
## iter  50 value 766.130133
## iter  60 value 722.293706
## iter  70 value 668.257294
## iter  80 value 636.712826
## iter  90 value 618.548626
## iter 100 value 598.051080
## iter 110 value 578.634890
## iter 120 value 544.075382
## iter 130 value 528.564691
## iter 140 value 515.171536
## iter 150 value 505.497297
## iter 160 value 490.666183
## iter 170 value 476.795460
## iter 180 value 464.203983
## iter 190 value 450.534716
## iter 200 value 442.057696
## iter 210 value 428.269413
## iter 220 value 415.716016
## iter 230 value 411.805372
## iter 240 value 400.500323
## iter 250 value 389.409199
## iter 260 value 377.573731
## iter 270 value 367.017041
## iter 280 value 354.721519
## iter 290 value 347.659602
## iter 300 value 341.802013
## iter 310 value 331.436035
## iter 320 value 326.980036
## iter 330 value 323.097620
## iter 340 value 321.484679
## iter 350 value 320.750515
## iter 360 value 320.357687
## iter 370 value 320.036586
## iter 380 value 319.386035
## iter 390 value 318.978826
## iter 400 value 318.632261
## iter 410 value 318.566028
## iter 420 value 318.398991
## iter 430 value 318.330713
## iter 440 value 318.325070
## iter 450 value 318.318671
## iter 460 value 318.314279
## iter 470 value 318.307885
## iter 480 value 318.298592
## iter 490 value 318.274969
## iter 500 value 318.251595
## final  value 318.251595 
## stopped after 500 iterations
## # weights:  141
## initial  value 1432813.655953 
## iter  10 value 2102.635737
## iter  20 value 1185.653353
## iter  30 value 936.188605
## iter  40 value 805.788597
## iter  50 value 728.498664
## iter  60 value 689.484378
## iter  70 value 636.591584
## iter  80 value 587.900354
## iter  90 value 550.708603
## iter 100 value 503.608683
## iter 110 value 477.530651
## iter 120 value 451.850853
## iter 130 value 425.495600
## iter 140 value 401.364566
## iter 150 value 375.137777
## iter 160 value 339.916821
## iter 170 value 317.178937
## iter 180 value 300.947840
## iter 190 value 290.725855
## iter 200 value 269.207375
## iter 210 value 253.732018
## iter 220 value 242.727055
## iter 230 value 234.262680
## iter 240 value 227.394285
## iter 250 value 223.483319
## iter 260 value 217.864359
## iter 270 value 214.152285
## iter 280 value 208.303563
## iter 290 value 205.782290
## iter 300 value 204.416298
## iter 310 value 202.398359
## iter 320 value 201.313649
## iter 330 value 199.420724
## iter 340 value 196.697899
## iter 350 value 194.264986
## iter 360 value 192.218276
## iter 370 value 188.909482
## iter 380 value 182.890105
## iter 390 value 179.637395
## iter 400 value 177.989207
## iter 410 value 174.742177
## iter 420 value 171.907411
## iter 430 value 169.385245
## iter 440 value 166.739346
## iter 450 value 164.619503
## iter 460 value 163.896160
## iter 470 value 163.223401
## iter 480 value 162.397472
## iter 490 value 162.182061
## iter 500 value 161.988318
## final  value 161.988318 
## stopped after 500 iterations
## # weights:  15
## initial  value 1390034.197438 
## iter  10 value 13892.124029
## iter  20 value 6562.188313
## iter  30 value 3051.444410
## iter  40 value 2127.421799
## iter  50 value 1876.204600
## iter  60 value 1826.133665
## iter  70 value 1802.185099
## iter  80 value 1791.544467
## iter  90 value 1787.494721
## iter 100 value 1785.775576
## iter 110 value 1783.312243
## iter 120 value 1782.484121
## final  value 1782.483480 
## converged
## # weights:  36
## initial  value 1366184.001165 
## iter  10 value 6654.551523
## iter  20 value 3417.026801
## iter  30 value 3157.240097
## iter  40 value 3043.722350
## iter  50 value 2927.381630
## iter  60 value 2812.134864
## iter  70 value 2226.318858
## iter  80 value 1869.583723
## iter  90 value 1703.540293
## iter 100 value 1581.067435
## iter 110 value 1507.632134
## iter 120 value 1477.089366
## iter 130 value 1448.334885
## iter 140 value 1432.439887
## iter 150 value 1422.224080
## iter 160 value 1411.459191
## iter 170 value 1408.426088
## iter 180 value 1402.467856
## iter 190 value 1398.844598
## iter 200 value 1392.989529
## iter 210 value 1392.297878
## iter 220 value 1392.110154
## iter 230 value 1391.997603
## iter 240 value 1391.940292
## iter 250 value 1391.821173
## iter 260 value 1391.703381
## iter 270 value 1391.586220
## iter 280 value 1391.563831
## iter 290 value 1391.550603
## iter 300 value 1391.537559
## iter 310 value 1391.505456
## iter 320 value 1391.495127
## iter 330 value 1391.477459
## iter 340 value 1391.450899
## final  value 1391.443955 
## converged
## # weights:  71
## initial  value 1391813.114842 
## iter  10 value 2657.100294
## iter  20 value 1634.379497
## iter  30 value 1252.532240
## iter  40 value 1131.097605
## iter  50 value 1059.148725
## iter  60 value 1021.456656
## iter  70 value 979.072678
## iter  80 value 922.713984
## iter  90 value 877.089914
## iter 100 value 833.580205
## iter 110 value 807.856865
## iter 120 value 786.434591
## iter 130 value 766.468394
## iter 140 value 755.635622
## iter 150 value 750.615626
## iter 160 value 748.927547
## iter 170 value 743.641113
## iter 180 value 723.671557
## iter 190 value 676.136056
## iter 200 value 646.064332
## iter 210 value 630.868904
## iter 220 value 628.582884
## iter 230 value 627.673696
## iter 240 value 627.206151
## iter 250 value 627.093884
## iter 260 value 627.035724
## iter 270 value 626.930539
## iter 280 value 626.799097
## iter 290 value 626.733688
## iter 300 value 626.720008
## iter 310 value 626.701682
## iter 320 value 626.682585
## iter 330 value 626.655762
## iter 340 value 626.640426
## iter 350 value 626.611153
## iter 360 value 626.581425
## iter 370 value 626.492494
## iter 380 value 626.297004
## iter 390 value 626.197880
## iter 400 value 626.142191
## iter 410 value 626.118176
## iter 420 value 626.110193
## iter 430 value 626.108231
## iter 430 value 626.108231
## final  value 626.108231 
## converged
## # weights:  106
## initial  value 1347737.515088 
## iter  10 value 1456.152890
## iter  20 value 1051.910948
## iter  30 value 957.944274
## iter  40 value 869.638904
## iter  50 value 803.947749
## iter  60 value 778.208427
## iter  70 value 755.243332
## iter  80 value 733.766593
## iter  90 value 712.589748
## iter 100 value 687.868965
## iter 110 value 659.591066
## iter 120 value 637.406535
## iter 130 value 620.310398
## iter 140 value 607.039906
## iter 150 value 590.667537
## iter 160 value 567.129722
## iter 170 value 548.984706
## iter 180 value 531.821781
## iter 190 value 524.878809
## iter 200 value 512.113325
## iter 210 value 500.144779
## iter 220 value 494.540525
## iter 230 value 491.317886
## iter 240 value 484.234859
## iter 250 value 477.131535
## iter 260 value 472.703976
## iter 270 value 470.226546
## iter 280 value 463.632302
## iter 290 value 457.317511
## iter 300 value 447.362587
## iter 310 value 442.759559
## iter 320 value 438.021809
## iter 330 value 434.094670
## iter 340 value 432.777422
## iter 350 value 431.375867
## iter 360 value 430.276205
## iter 370 value 429.770074
## iter 380 value 428.890657
## iter 390 value 427.135000
## iter 400 value 424.457959
## iter 410 value 421.697228
## iter 420 value 420.369075
## iter 430 value 419.803702
## iter 440 value 419.708702
## iter 450 value 419.599151
## iter 460 value 419.418681
## iter 470 value 418.998346
## iter 480 value 416.298606
## iter 490 value 413.662347
## iter 500 value 411.517987
## final  value 411.517987 
## stopped after 500 iterations
## # weights:  141
## initial  value 1420653.045091 
## iter  10 value 1711.164326
## iter  20 value 1110.907664
## iter  30 value 974.478876
## iter  40 value 877.348897
## iter  50 value 782.091139
## iter  60 value 679.990586
## iter  70 value 626.639526
## iter  80 value 557.541007
## iter  90 value 528.641102
## iter 100 value 498.316283
## iter 110 value 467.256378
## iter 120 value 439.128405
## iter 130 value 417.336519
## iter 140 value 387.636253
## iter 150 value 350.199237
## iter 160 value 316.187138
## iter 170 value 289.024498
## iter 180 value 268.293152
## iter 190 value 254.327908
## iter 200 value 242.466847
## iter 210 value 233.942305
## iter 220 value 221.951709
## iter 230 value 216.097933
## iter 240 value 209.986860
## iter 250 value 203.126497
## iter 260 value 198.046217
## iter 270 value 193.460140
## iter 280 value 190.334481
## iter 290 value 188.691913
## iter 300 value 187.153414
## iter 310 value 183.927521
## iter 320 value 180.992975
## iter 330 value 178.408392
## iter 340 value 175.608049
## iter 350 value 171.951506
## iter 360 value 169.115878
## iter 370 value 167.319167
## iter 380 value 165.323391
## iter 390 value 164.055264
## iter 400 value 161.973875
## iter 410 value 160.371288
## iter 420 value 158.622948
## iter 430 value 156.363277
## iter 440 value 154.606331
## iter 450 value 151.958988
## iter 460 value 148.972841
## iter 470 value 146.953390
## iter 480 value 145.769754
## iter 490 value 144.810802
## iter 500 value 144.295475
## final  value 144.295475 
## stopped after 500 iterations
## # weights:  15
## initial  value 1424069.190252 
## iter  10 value 6635.879391
## iter  20 value 5903.955599
## iter  30 value 5621.027754
## iter  40 value 5329.960127
## iter  50 value 5307.171748
## iter  60 value 5148.468381
## iter  70 value 3040.862786
## iter  80 value 2000.619272
## iter  90 value 1867.171755
## iter 100 value 1826.648976
## iter 110 value 1805.111883
## iter 120 value 1795.580194
## iter 130 value 1795.533810
## final  value 1795.533624 
## converged
## # weights:  36
## initial  value 1443980.895185 
## iter  10 value 7280.403142
## iter  20 value 4085.072536
## iter  30 value 2975.278007
## iter  40 value 1847.289436
## iter  50 value 1428.688958
## iter  60 value 1278.330422
## iter  70 value 1210.312262
## iter  80 value 1187.209394
## iter  90 value 1172.772049
## iter 100 value 1134.732123
## iter 110 value 1097.817867
## iter 120 value 1078.813174
## iter 130 value 1067.060421
## iter 140 value 1062.209518
## iter 150 value 1045.276789
## iter 160 value 1025.356024
## iter 170 value 1008.443855
## iter 180 value 1008.041913
## iter 190 value 1001.292018
## iter 200 value 993.480759
## iter 210 value 990.678807
## iter 220 value 987.734482
## iter 230 value 984.658447
## iter 240 value 982.672619
## iter 250 value 982.484801
## iter 260 value 982.388583
## iter 270 value 981.554356
## iter 280 value 981.399040
## iter 290 value 981.335401
## iter 300 value 981.137161
## iter 310 value 980.797101
## iter 320 value 980.779059
## iter 330 value 980.778186
## iter 340 value 980.749443
## iter 350 value 980.742110
## iter 360 value 980.733796
## iter 370 value 980.671317
## iter 380 value 980.602457
## iter 390 value 980.582106
## iter 400 value 980.581640
## final  value 980.581611 
## converged
## # weights:  71
## initial  value 1374791.289966 
## iter  10 value 4182.123958
## iter  20 value 2436.759607
## iter  30 value 1745.431098
## iter  40 value 1391.255354
## iter  50 value 1108.066002
## iter  60 value 943.730878
## iter  70 value 886.324368
## iter  80 value 825.999663
## iter  90 value 779.728138
## iter 100 value 755.351732
## iter 110 value 741.211483
## iter 120 value 734.594663
## iter 130 value 732.136351
## iter 140 value 729.832074
## iter 150 value 728.256347
## iter 160 value 727.810340
## iter 170 value 727.651120
## iter 180 value 727.607532
## iter 190 value 727.531994
## iter 200 value 727.506774
## iter 210 value 727.464843
## iter 220 value 727.438555
## iter 230 value 727.348023
## iter 240 value 727.248679
## iter 250 value 727.182550
## iter 260 value 727.144778
## iter 270 value 727.122134
## iter 280 value 727.109875
## iter 290 value 727.097412
## iter 300 value 727.089414
## iter 310 value 727.084121
## final  value 727.084095 
## converged
## # weights:  106
## initial  value 1386376.518562 
## iter  10 value 1247.294295
## iter  20 value 1038.179311
## iter  30 value 909.896658
## iter  40 value 823.815879
## iter  50 value 769.363055
## iter  60 value 720.898727
## iter  70 value 678.169966
## iter  80 value 647.781180
## iter  90 value 617.934369
## iter 100 value 563.129779
## iter 110 value 490.547015
## iter 120 value 429.173335
## iter 130 value 408.650063
## iter 140 value 377.261748
## iter 150 value 358.575244
## iter 160 value 346.642453
## iter 170 value 338.871959
## iter 180 value 334.002855
## iter 190 value 329.980320
## iter 200 value 327.466104
## iter 210 value 325.287960
## iter 220 value 323.993941
## iter 230 value 323.070865
## iter 240 value 321.718002
## iter 250 value 320.341605
## iter 260 value 319.149367
## iter 270 value 317.164050
## iter 280 value 315.816459
## iter 290 value 313.156483
## iter 300 value 309.147917
## iter 310 value 304.287366
## iter 320 value 300.633211
## iter 330 value 295.338694
## iter 340 value 292.412166
## iter 350 value 289.735876
## iter 360 value 287.979883
## iter 370 value 284.168822
## iter 380 value 272.178097
## iter 390 value 266.121787
## iter 400 value 264.037361
## iter 410 value 263.215245
## iter 420 value 261.608534
## iter 430 value 260.698217
## iter 440 value 260.617884
## iter 450 value 260.481716
## iter 460 value 260.410018
## iter 470 value 260.349893
## iter 480 value 260.074588
## iter 490 value 260.032616
## iter 500 value 259.955186
## final  value 259.955186 
## stopped after 500 iterations
## # weights:  141
## initial  value 1409018.705112 
## iter  10 value 1844.575466
## iter  20 value 1111.140329
## iter  30 value 964.658936
## iter  40 value 884.317005
## iter  50 value 776.252817
## iter  60 value 692.961189
## iter  70 value 633.867363
## iter  80 value 571.794127
## iter  90 value 522.391308
## iter 100 value 499.615265
## iter 110 value 476.708638
## iter 120 value 454.965722
## iter 130 value 435.481181
## iter 140 value 414.278938
## iter 150 value 385.732347
## iter 160 value 367.876911
## iter 170 value 347.448267
## iter 180 value 329.063970
## iter 190 value 309.370327
## iter 200 value 290.847554
## iter 210 value 278.631944
## iter 220 value 265.429359
## iter 230 value 253.150354
## iter 240 value 246.538322
## iter 250 value 241.199468
## iter 260 value 236.388921
## iter 270 value 233.155801
## iter 280 value 230.945762
## iter 290 value 229.819392
## iter 300 value 229.011413
## iter 310 value 228.007311
## iter 320 value 227.032619
## iter 330 value 226.056214
## iter 340 value 224.324973
## iter 350 value 221.525407
## iter 360 value 219.055554
## iter 370 value 216.455249
## iter 380 value 213.844314
## iter 390 value 209.720402
## iter 400 value 202.645145
## iter 410 value 197.949226
## iter 420 value 194.761329
## iter 430 value 192.530156
## iter 440 value 191.356093
## iter 450 value 190.046195
## iter 460 value 189.086656
## iter 470 value 188.074922
## iter 480 value 187.271616
## iter 490 value 186.690422
## iter 500 value 186.043633
## final  value 186.043633 
## stopped after 500 iterations
## # weights:  15
## initial  value 1402720.581926 
## iter  10 value 10320.611162
## iter  20 value 9033.891051
## iter  30 value 7754.065428
## iter  40 value 6072.915906
## iter  50 value 3738.458154
## iter  60 value 2320.873572
## iter  70 value 1797.419317
## iter  80 value 1616.992327
## iter  90 value 1589.411758
## iter 100 value 1542.523117
## iter 110 value 1515.046126
## iter 120 value 1510.328549
## iter 130 value 1509.538536
## final  value 1509.505079 
## converged
## # weights:  36
## initial  value 1386194.489221 
## iter  10 value 101875.460853
## iter  20 value 32959.671045
## iter  30 value 9987.939720
## iter  40 value 5698.298534
## iter  50 value 4283.741617
## iter  60 value 2501.632114
## iter  70 value 1937.359968
## iter  80 value 1552.409305
## iter  90 value 1393.179486
## iter 100 value 1339.420470
## iter 110 value 1311.804863
## iter 120 value 1283.072744
## iter 130 value 1216.184077
## iter 140 value 1182.339946
## iter 150 value 1178.101608
## iter 160 value 1177.042523
## iter 170 value 1176.411048
## iter 180 value 1175.705566
## iter 190 value 1175.403006
## iter 200 value 1174.711678
## iter 210 value 1173.689247
## iter 220 value 1172.612147
## iter 230 value 1172.159412
## iter 240 value 1172.013974
## final  value 1172.010770 
## converged
## # weights:  71
## initial  value 1396053.569499 
## iter  10 value 3348.408305
## iter  20 value 2030.413248
## iter  30 value 1668.129944
## iter  40 value 1464.036382
## iter  50 value 1398.572290
## iter  60 value 1308.307907
## iter  70 value 1245.618153
## iter  80 value 1198.630055
## iter  90 value 1172.895225
## iter 100 value 1105.224645
## iter 110 value 1064.487764
## iter 120 value 1042.163154
## iter 130 value 1030.645278
## iter 140 value 1024.109557
## iter 150 value 1021.947389
## iter 160 value 1017.164075
## iter 170 value 1013.411143
## iter 180 value 1011.925871
## iter 190 value 1011.391476
## iter 200 value 1011.095843
## iter 210 value 1010.946906
## iter 220 value 1008.553339
## iter 230 value 1006.690289
## iter 240 value 1006.566334
## final  value 1006.560429 
## converged
## # weights:  106
## initial  value 1391516.432562 
## iter  10 value 1651.167238
## iter  20 value 1251.083304
## iter  30 value 1115.024091
## iter  40 value 1004.709583
## iter  50 value 955.982378
## iter  60 value 916.445844
## iter  70 value 890.251077
## iter  80 value 870.628227
## iter  90 value 853.357247
## iter 100 value 839.758639
## iter 110 value 829.431755
## iter 120 value 822.898385
## iter 130 value 815.269611
## iter 140 value 800.052318
## iter 150 value 787.450236
## iter 160 value 773.064706
## iter 170 value 759.697076
## iter 180 value 752.004200
## iter 190 value 740.734903
## iter 200 value 730.634272
## iter 210 value 726.799258
## iter 220 value 724.198612
## iter 230 value 720.027947
## iter 240 value 711.241997
## iter 250 value 704.217090
## iter 260 value 696.400582
## iter 270 value 693.056135
## iter 280 value 690.672336
## iter 290 value 687.886481
## iter 300 value 683.845858
## iter 310 value 681.197153
## iter 320 value 679.005369
## iter 330 value 677.799764
## iter 340 value 676.728619
## iter 350 value 676.424643
## iter 360 value 676.394985
## iter 370 value 676.394244
## final  value 676.394219 
## converged
## # weights:  141
## initial  value 1431966.362448 
## iter  10 value 1490.706632
## iter  20 value 1209.389376
## iter  30 value 1086.612162
## iter  40 value 1021.204629
## iter  50 value 959.489318
## iter  60 value 890.834848
## iter  70 value 855.744224
## iter  80 value 829.290153
## iter  90 value 793.608643
## iter 100 value 768.599793
## iter 110 value 744.324212
## iter 120 value 726.997448
## iter 130 value 707.120376
## iter 140 value 689.025439
## iter 150 value 681.849741
## iter 160 value 672.184899
## iter 170 value 663.316771
## iter 180 value 655.752892
## iter 190 value 649.283134
## iter 200 value 641.346472
## iter 210 value 634.067406
## iter 220 value 628.673020
## iter 230 value 620.702521
## iter 240 value 612.467706
## iter 250 value 609.368916
## iter 260 value 604.927903
## iter 270 value 599.893364
## iter 280 value 596.409329
## iter 290 value 595.198555
## iter 300 value 593.794668
## iter 310 value 591.448377
## iter 320 value 589.881803
## iter 330 value 589.251423
## iter 340 value 588.660829
## iter 350 value 588.011147
## iter 360 value 587.094786
## iter 370 value 585.945002
## iter 380 value 583.215844
## iter 390 value 581.510017
## iter 400 value 581.068589
## iter 410 value 580.852576
## iter 420 value 580.803359
## iter 430 value 580.769404
## iter 440 value 580.762703
## iter 450 value 580.760563
## final  value 580.760385 
## converged
## # weights:  15
## initial  value 1397002.019183 
## iter  10 value 20629.923574
## iter  20 value 11027.841073
## iter  30 value 5856.981940
## iter  40 value 5142.641439
## iter  50 value 4209.857075
## iter  60 value 2361.599064
## iter  70 value 2064.151928
## iter  80 value 1847.053856
## iter  90 value 1823.885867
## iter 100 value 1814.551563
## iter 110 value 1804.181113
## iter 120 value 1800.767789
## iter 130 value 1799.632021
## iter 140 value 1799.219463
## iter 150 value 1794.636456
## iter 160 value 1773.915327
## iter 170 value 1681.441716
## iter 180 value 1659.948644
## final  value 1659.423588 
## converged
## # weights:  36
## initial  value 1414096.251600 
## iter  10 value 2709.437795
## iter  20 value 1788.897752
## iter  30 value 1391.267360
## iter  40 value 1223.880107
## iter  50 value 1144.054983
## iter  60 value 1098.565361
## iter  70 value 1087.314627
## iter  80 value 1084.105204
## iter  90 value 1081.465356
## iter 100 value 1073.438628
## iter 110 value 1065.763494
## iter 120 value 1039.704350
## iter 130 value 986.244055
## iter 140 value 970.564976
## iter 150 value 965.563762
## iter 160 value 963.367023
## iter 170 value 958.152862
## iter 180 value 945.289910
## iter 190 value 940.633947
## iter 200 value 940.321929
## iter 210 value 940.255243
## iter 220 value 940.253476
## iter 220 value 940.253476
## final  value 940.253476 
## converged
## # weights:  71
## initial  value 1394253.632215 
## iter  10 value 3200.628477
## iter  20 value 1735.099501
## iter  30 value 1322.297661
## iter  40 value 1099.320053
## iter  50 value 999.444518
## iter  60 value 920.008910
## iter  70 value 896.405903
## iter  80 value 872.327153
## iter  90 value 845.528394
## iter 100 value 807.287629
## iter 110 value 745.245386
## iter 120 value 717.940599
## iter 130 value 698.521190
## iter 140 value 692.137863
## iter 150 value 690.002982
## iter 160 value 688.491148
## iter 170 value 687.464151
## iter 180 value 685.611881
## iter 190 value 680.417343
## iter 200 value 675.012656
## iter 210 value 671.782331
## iter 220 value 671.287493
## iter 230 value 669.802195
## iter 240 value 665.191673
## iter 250 value 659.674633
## iter 260 value 653.609435
## iter 270 value 648.654302
## iter 280 value 647.029240
## iter 290 value 645.609802
## iter 300 value 645.188807
## iter 310 value 645.168340
## iter 320 value 645.165064
## final  value 645.164719 
## converged
## # weights:  106
## initial  value 1415252.908137 
## iter  10 value 1736.174153
## iter  20 value 1149.296464
## iter  30 value 911.536294
## iter  40 value 810.067270
## iter  50 value 752.509179
## iter  60 value 679.659254
## iter  70 value 626.473432
## iter  80 value 585.425804
## iter  90 value 548.181105
## iter 100 value 523.018442
## iter 110 value 505.674216
## iter 120 value 498.789073
## iter 130 value 492.719132
## iter 140 value 486.714055
## iter 150 value 477.469363
## iter 160 value 469.654197
## iter 170 value 459.895522
## iter 180 value 451.245040
## iter 190 value 447.538415
## iter 200 value 443.426293
## iter 210 value 440.581463
## iter 220 value 439.294453
## iter 230 value 438.385818
## iter 240 value 435.053121
## iter 250 value 429.012767
## iter 260 value 421.840405
## iter 270 value 418.507067
## iter 280 value 413.529762
## iter 290 value 399.861418
## iter 300 value 394.881998
## iter 310 value 391.752172
## iter 320 value 385.919022
## iter 330 value 382.142778
## iter 340 value 377.223991
## iter 350 value 370.636767
## iter 360 value 363.643239
## iter 370 value 361.159591
## iter 380 value 359.541919
## iter 390 value 358.772838
## iter 400 value 358.511118
## iter 410 value 358.411810
## iter 420 value 358.240310
## iter 430 value 358.095445
## iter 440 value 358.062397
## iter 450 value 358.019919
## iter 460 value 357.945672
## iter 470 value 357.866273
## iter 480 value 357.796127
## iter 490 value 357.621143
## iter 500 value 357.390823
## final  value 357.390823 
## stopped after 500 iterations
## # weights:  141
## initial  value 1312625.836615 
## iter  10 value 1413.188581
## iter  20 value 1121.974937
## iter  30 value 954.382988
## iter  40 value 859.442334
## iter  50 value 753.920929
## iter  60 value 688.836022
## iter  70 value 647.293677
## iter  80 value 605.205853
## iter  90 value 538.787800
## iter 100 value 483.570881
## iter 110 value 449.156990
## iter 120 value 419.509644
## iter 130 value 394.513559
## iter 140 value 379.894016
## iter 150 value 368.220254
## iter 160 value 361.684027
## iter 170 value 355.353624
## iter 180 value 347.444538
## iter 190 value 342.048100
## iter 200 value 334.999553
## iter 210 value 329.043016
## iter 220 value 321.844182
## iter 230 value 315.803421
## iter 240 value 310.175060
## iter 250 value 307.852459
## iter 260 value 304.620041
## iter 270 value 302.315840
## iter 280 value 300.639048
## iter 290 value 299.409899
## iter 300 value 298.711622
## iter 310 value 297.499185
## iter 320 value 295.866229
## iter 330 value 293.362930
## iter 340 value 288.647338
## iter 350 value 284.678824
## iter 360 value 279.715284
## iter 370 value 275.806177
## iter 380 value 272.317030
## iter 390 value 267.148309
## iter 400 value 262.518896
## iter 410 value 259.120087
## iter 420 value 255.959338
## iter 430 value 253.864496
## iter 440 value 252.479472
## iter 450 value 251.100340
## iter 460 value 249.150001
## iter 470 value 243.644885
## iter 480 value 241.255169
## iter 490 value 238.884555
## iter 500 value 237.091930
## final  value 237.091930 
## stopped after 500 iterations
## # weights:  15
## initial  value 1413533.660184 
## iter  10 value 4721.894089
## iter  20 value 3416.384627
## iter  30 value 1927.536574
## iter  40 value 1800.717388
## iter  50 value 1666.988726
## iter  60 value 1641.844646
## iter  70 value 1575.835349
## iter  80 value 1507.321630
## iter  90 value 1444.003188
## iter 100 value 1274.923782
## iter 110 value 1259.461330
## iter 120 value 1253.175657
## iter 130 value 1236.620574
## iter 140 value 1230.732785
## iter 150 value 1228.920349
## iter 160 value 1225.804320
## iter 170 value 1223.734911
## iter 180 value 1223.569297
## iter 190 value 1222.930499
## iter 200 value 1222.541861
## iter 210 value 1222.487218
## iter 220 value 1222.162071
## iter 230 value 1221.655654
## iter 240 value 1221.612406
## iter 250 value 1221.582362
## iter 260 value 1221.519695
## final  value 1221.516965 
## converged
## # weights:  36
## initial  value 1392306.769012 
## iter  10 value 3495.866684
## iter  20 value 2586.700688
## iter  30 value 2393.753491
## iter  40 value 2131.211720
## iter  50 value 2057.587791
## iter  60 value 2017.415093
## iter  70 value 1853.602011
## iter  80 value 1683.490499
## iter  90 value 1505.011661
## iter 100 value 1338.334097
## iter 110 value 1200.576569
## iter 120 value 1179.235350
## iter 130 value 1167.690896
## iter 140 value 1162.949527
## iter 150 value 1157.261885
## iter 160 value 1156.575128
## iter 170 value 1156.552972
## iter 180 value 1156.329713
## iter 190 value 1156.279126
## iter 200 value 1154.874673
## iter 210 value 1153.741750
## iter 220 value 1153.678035
## iter 230 value 1151.277530
## iter 240 value 1150.348107
## final  value 1150.244051 
## converged
## # weights:  71
## initial  value 1430457.741955 
## iter  10 value 2111.775832
## iter  20 value 1196.036583
## iter  30 value 1054.648346
## iter  40 value 952.624033
## iter  50 value 903.238783
## iter  60 value 857.466852
## iter  70 value 825.161480
## iter  80 value 791.499398
## iter  90 value 771.815535
## iter 100 value 740.380278
## iter 110 value 724.700888
## iter 120 value 715.402902
## iter 130 value 703.822034
## iter 140 value 692.671990
## iter 150 value 687.258316
## iter 160 value 683.371189
## iter 170 value 675.911088
## iter 180 value 670.068800
## iter 190 value 656.565668
## iter 200 value 645.747245
## iter 210 value 636.675883
## iter 220 value 630.720232
## iter 230 value 617.075232
## iter 240 value 608.103878
## iter 250 value 603.319656
## iter 260 value 601.952011
## iter 270 value 597.352497
## iter 280 value 594.656861
## iter 290 value 592.652158
## iter 300 value 590.060822
## iter 310 value 588.307095
## iter 320 value 584.803095
## iter 330 value 580.003906
## iter 340 value 578.884971
## iter 350 value 578.644450
## iter 360 value 578.040543
## iter 370 value 577.074206
## iter 380 value 573.990947
## iter 390 value 572.418554
## iter 400 value 572.002550
## iter 410 value 571.070268
## iter 420 value 570.537695
## iter 430 value 570.431509
## iter 440 value 570.045800
## iter 450 value 568.596114
## iter 460 value 566.745335
## iter 470 value 566.000581
## iter 480 value 564.856702
## iter 490 value 564.784314
## iter 500 value 564.616962
## final  value 564.616962 
## stopped after 500 iterations
## # weights:  106
## initial  value 1461657.112234 
## iter  10 value 1739.933571
## iter  20 value 1129.764524
## iter  30 value 944.472279
## iter  40 value 841.536783
## iter  50 value 790.010241
## iter  60 value 739.058525
## iter  70 value 700.215976
## iter  80 value 675.755498
## iter  90 value 653.998602
## iter 100 value 627.266987
## iter 110 value 606.752424
## iter 120 value 582.285879
## iter 130 value 564.673206
## iter 140 value 546.879996
## iter 150 value 529.985040
## iter 160 value 513.233184
## iter 170 value 498.716100
## iter 180 value 474.959332
## iter 190 value 458.375545
## iter 200 value 446.665097
## iter 210 value 432.422076
## iter 220 value 426.593379
## iter 230 value 424.377783
## iter 240 value 413.280879
## iter 250 value 405.589091
## iter 260 value 392.814315
## iter 270 value 383.194085
## iter 280 value 374.911029
## iter 290 value 364.397982
## iter 300 value 353.497896
## iter 310 value 342.664572
## iter 320 value 335.903219
## iter 330 value 332.966207
## iter 340 value 332.199813
## iter 350 value 331.649081
## iter 360 value 331.210729
## iter 370 value 330.898721
## iter 380 value 329.693599
## iter 390 value 327.573093
## iter 400 value 326.216246
## iter 410 value 325.977657
## iter 420 value 325.741942
## iter 430 value 325.608225
## iter 440 value 325.591001
## iter 450 value 325.558885
## iter 460 value 325.506084
## iter 470 value 325.411805
## iter 480 value 325.303592
## iter 490 value 325.192868
## iter 500 value 324.892144
## final  value 324.892144 
## stopped after 500 iterations
## # weights:  141
## initial  value 1426004.080425 
## iter  10 value 1657.822788
## iter  20 value 1208.709292
## iter  30 value 1029.073809
## iter  40 value 900.484445
## iter  50 value 783.050196
## iter  60 value 702.087542
## iter  70 value 645.425211
## iter  80 value 602.557742
## iter  90 value 537.711814
## iter 100 value 504.170713
## iter 110 value 471.346255
## iter 120 value 450.171713
## iter 130 value 430.162608
## iter 140 value 406.953514
## iter 150 value 389.325455
## iter 160 value 374.075461
## iter 170 value 363.409511
## iter 180 value 354.457831
## iter 190 value 340.870779
## iter 200 value 329.537819
## iter 210 value 310.796943
## iter 220 value 298.272530
## iter 230 value 287.306282
## iter 240 value 276.529411
## iter 250 value 266.324739
## iter 260 value 261.078707
## iter 270 value 255.930320
## iter 280 value 251.011927
## iter 290 value 248.903234
## iter 300 value 247.621877
## iter 310 value 246.093950
## iter 320 value 243.733781
## iter 330 value 240.558099
## iter 340 value 235.914055
## iter 350 value 231.871406
## iter 360 value 227.510080
## iter 370 value 224.205130
## iter 380 value 220.245469
## iter 390 value 212.807360
## iter 400 value 208.670687
## iter 410 value 206.763314
## iter 420 value 205.485329
## iter 430 value 203.864056
## iter 440 value 202.487586
## iter 450 value 200.880771
## iter 460 value 200.316046
## iter 470 value 199.921837
## iter 480 value 199.252679
## iter 490 value 197.999137
## iter 500 value 197.005525
## final  value 197.005525 
## stopped after 500 iterations
## # weights:  15
## initial  value 1408432.479508 
## iter  10 value 15171.895631
## iter  20 value 6792.528075
## iter  30 value 3106.481551
## iter  40 value 2605.690059
## iter  50 value 2042.786602
## iter  60 value 1713.140333
## iter  70 value 1696.961086
## iter  80 value 1660.309124
## iter  90 value 1645.545975
## iter 100 value 1643.237576
## iter 110 value 1641.871108
## iter 120 value 1635.572779
## iter 130 value 1633.842065
## iter 140 value 1633.714580
## iter 150 value 1631.474656
## iter 160 value 1629.589204
## iter 170 value 1629.567523
## iter 180 value 1628.106566
## iter 190 value 1627.055879
## iter 200 value 1626.921655
## iter 210 value 1626.429491
## iter 220 value 1625.880181
## iter 230 value 1625.851738
## iter 240 value 1625.837352
## iter 250 value 1625.595330
## iter 260 value 1625.500214
## final  value 1625.500173 
## converged
## # weights:  36
## initial  value 1424448.457589 
## iter  10 value 4575.681223
## iter  20 value 2686.100236
## iter  30 value 2247.465147
## iter  40 value 1782.994432
## iter  50 value 1403.011423
## iter  60 value 1247.919580
## iter  70 value 1222.091882
## iter  80 value 1208.664119
## iter  90 value 1202.077338
## iter 100 value 1186.449513
## iter 110 value 1171.599896
## iter 120 value 1167.843354
## iter 130 value 1163.283684
## iter 140 value 1160.881349
## iter 150 value 1160.530210
## iter 160 value 1160.500446
## iter 170 value 1160.395427
## iter 180 value 1160.271370
## iter 190 value 1160.179868
## iter 200 value 1160.148109
## iter 210 value 1160.000179
## iter 220 value 1159.491818
## iter 230 value 1159.264864
## iter 240 value 1158.627279
## iter 250 value 1155.722644
## iter 260 value 1154.730701
## iter 270 value 1154.647463
## iter 280 value 1154.535382
## iter 290 value 1154.498254
## iter 300 value 1154.488706
## iter 310 value 1154.488246
## final  value 1154.488196 
## converged
## # weights:  71
## initial  value 1417557.681487 
## iter  10 value 2080.368532
## iter  20 value 1381.057850
## iter  30 value 1142.244345
## iter  40 value 934.010545
## iter  50 value 850.350536
## iter  60 value 797.620170
## iter  70 value 767.714937
## iter  80 value 738.635854
## iter  90 value 720.785523
## iter 100 value 679.792567
## iter 110 value 656.579060
## iter 120 value 641.181615
## iter 130 value 630.408704
## iter 140 value 619.613237
## iter 150 value 611.032133
## iter 160 value 609.037849
## iter 170 value 605.680429
## iter 180 value 600.468600
## iter 190 value 592.089937
## iter 200 value 578.679366
## iter 210 value 570.460557
## iter 220 value 561.256566
## iter 230 value 550.447676
## iter 240 value 545.834065
## iter 250 value 542.819047
## iter 260 value 541.366544
## iter 270 value 540.492214
## iter 280 value 540.215353
## iter 290 value 539.994488
## iter 300 value 539.949136
## iter 310 value 539.901212
## iter 320 value 539.839825
## iter 330 value 539.721864
## iter 340 value 539.328052
## iter 350 value 539.138437
## iter 360 value 538.758330
## iter 370 value 538.473643
## iter 380 value 538.199832
## iter 390 value 537.976579
## iter 400 value 537.780806
## iter 410 value 537.645212
## iter 420 value 537.530034
## iter 430 value 537.438562
## iter 440 value 537.436739
## iter 450 value 537.406417
## iter 460 value 537.336946
## iter 470 value 537.242874
## iter 480 value 537.101962
## iter 490 value 536.937019
## iter 500 value 536.788472
## final  value 536.788472 
## stopped after 500 iterations
## # weights:  106
## initial  value 1460565.877414 
## iter  10 value 2604.696195
## iter  20 value 1231.521063
## iter  30 value 1043.394233
## iter  40 value 948.020052
## iter  50 value 833.118880
## iter  60 value 737.124196
## iter  70 value 691.535713
## iter  80 value 661.927489
## iter  90 value 621.981945
## iter 100 value 592.558131
## iter 110 value 560.419370
## iter 120 value 535.872650
## iter 130 value 512.997449
## iter 140 value 504.306197
## iter 150 value 496.839702
## iter 160 value 487.764107
## iter 170 value 481.545347
## iter 180 value 476.667601
## iter 190 value 473.778281
## iter 200 value 471.991909
## iter 210 value 468.848764
## iter 220 value 467.471528
## iter 230 value 466.745202
## iter 240 value 464.394145
## iter 250 value 461.843272
## iter 260 value 454.347278
## iter 270 value 450.364408
## iter 280 value 447.799654
## iter 290 value 445.246241
## iter 300 value 441.806379
## iter 310 value 440.266362
## iter 320 value 437.451780
## iter 330 value 431.787413
## iter 340 value 422.709456
## iter 350 value 414.622455
## iter 360 value 408.971817
## iter 370 value 404.770230
## iter 380 value 402.919852
## iter 390 value 401.756895
## iter 400 value 400.851514
## iter 410 value 399.982132
## iter 420 value 399.406058
## iter 430 value 398.852108
## iter 440 value 398.713542
## iter 450 value 398.434726
## iter 460 value 398.097170
## iter 470 value 397.907875
## iter 480 value 397.747425
## iter 490 value 397.677596
## iter 500 value 397.622073
## final  value 397.622073 
## stopped after 500 iterations
## # weights:  141
## initial  value 1390515.760772 
## iter  10 value 1573.988546
## iter  20 value 1109.118264
## iter  30 value 964.094875
## iter  40 value 858.884226
## iter  50 value 771.939768
## iter  60 value 719.022903
## iter  70 value 645.597443
## iter  80 value 576.288972
## iter  90 value 540.378345
## iter 100 value 499.671779
## iter 110 value 474.404980
## iter 120 value 451.229096
## iter 130 value 430.808501
## iter 140 value 409.024914
## iter 150 value 393.055717
## iter 160 value 377.187053
## iter 170 value 361.390700
## iter 180 value 353.273033
## iter 190 value 346.860592
## iter 200 value 338.202899
## iter 210 value 328.800874
## iter 220 value 322.312555
## iter 230 value 315.232826
## iter 240 value 306.416530
## iter 250 value 298.244246
## iter 260 value 291.495383
## iter 270 value 282.160253
## iter 280 value 263.279304
## iter 290 value 257.664835
## iter 300 value 254.656368
## iter 310 value 248.739981
## iter 320 value 244.706123
## iter 330 value 239.372479
## iter 340 value 235.700617
## iter 350 value 230.732967
## iter 360 value 223.326551
## iter 370 value 216.920794
## iter 380 value 212.856378
## iter 390 value 206.050621
## iter 400 value 197.978055
## iter 410 value 194.327983
## iter 420 value 192.579616
## iter 430 value 190.283316
## iter 440 value 188.152264
## iter 450 value 186.594779
## iter 460 value 184.864735
## iter 470 value 183.956478
## iter 480 value 183.442826
## iter 490 value 182.820518
## iter 500 value 182.093269
## final  value 182.093269 
## stopped after 500 iterations
## # weights:  15
## initial  value 1527080.175508 
## iter  10 value 102805.349590
## iter  20 value 18306.866180
## iter  30 value 11220.529339
## iter  40 value 9758.335008
## iter  50 value 5584.599486
## iter  60 value 2593.036654
## iter  70 value 2314.360343
## iter  80 value 2074.127736
## iter  90 value 1707.529792
## iter 100 value 1632.459034
## iter 110 value 1620.942217
## iter 120 value 1601.733505
## iter 130 value 1600.435375
## iter 140 value 1600.420419
## final  value 1600.408066 
## converged
##################################
# Reporting the cross-validation results
# for the NN model
##################################
NN_Tune
## Neural Network 
## 
## 294 samples
##   5 predictor
## 
## Pre-processing: centered (4), scaled (4), ignore (1) 
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 266, 264, 265, 264, 265, 266, ... 
## Resampling results across tuning parameters:
## 
##   size  decay  RMSE      Rsquared   MAE     
##    2    0e+00  3.459517  0.8369414  2.761631
##    2    1e-05  2.493429  0.9074457  1.926148
##    2    1e-04  2.477974  0.9067875  1.874481
##    2    1e-03  2.426691  0.9115333  1.809876
##    2    1e-01  2.353487  0.9162047  1.783663
##    5    0e+00  3.983000  0.7736723  2.182406
##    5    1e-05  2.545260  0.9014702  1.936831
##    5    1e-04  2.917347  0.8648001  1.990064
##    5    1e-03  2.579639  0.8959853  1.848074
##    5    1e-01  2.560729  0.9044140  1.805113
##   10    0e+00  2.941692  0.8734155  2.195624
##   10    1e-05  5.013236  0.7454131  2.569115
##   10    1e-04  4.104210  0.7609624  2.385276
##   10    1e-03  3.199424  0.8511775  2.303877
##   10    1e-01  2.831829  0.8769466  2.114228
##   15    0e+00  4.024030  0.7486857  2.691372
##   15    1e-05  4.260785  0.7695307  2.715268
##   15    1e-04  3.107588  0.8600351  2.333819
##   15    1e-03  3.461486  0.8201023  2.585248
##   15    1e-01  2.914087  0.8805032  2.135890
##   20    0e+00  4.050885  0.7894326  2.908419
##   20    1e-05  4.135612  0.7632034  2.943303
##   20    1e-04  3.706096  0.8182675  2.825326
##   20    1e-03  4.300572  0.7507562  3.033962
##   20    1e-01  3.155988  0.8634645  2.332871
## 
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were size = 2 and decay = 0.1.
NN_Tune$finalModel
## a 5-2-1 network with 15 weights
## inputs: GENDERFemale INFMOR PERCAP CLTECH NCOMOR 
## output(s): .outcome 
## options were - linear output units  decay=0.1
(NN_Tune_RMSE <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
                              NN_Tune$results$decay==NN_Tune$bestTune$decay,
                 c("RMSE")])
## [1] 2.353487
(NN_Tune_Rsquared <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
                              NN_Tune$results$decay==NN_Tune$bestTune$decay,
                 c("Rsquared")])
## [1] 0.9162047
(NN_Tune_MAE <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
                              NN_Tune$results$decay==NN_Tune$bestTune$decay,
                 c("MAE")])
## [1] 1.783663
##################################
# Identifying and plotting the
# best model predictors
# for the NN model
##################################
NN_VarImp <- varImp(NN_Tune, scale = TRUE)
plot(NN_VarImp, 
     scales=list(y=list(cex = .95)),
     main="Ranked Variable Importance : NN",
     xlab="Scaled Variable Importance Metrics",
     ylab="Predictors",
     cex=2,
     origin=0,
     alpha=0.45)


1.3.6.4 Partial Least Squares Regression (PLS)


Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the PLS model
##################################
PLS_Grid = expand.grid(ncomp = 1:5)

##################################
# Running the PLS model
# by setting the caret method to 'pls'
##################################
set.seed(12345678)
PLS_Tune <- train(x = MD.Model.Predictors,
                  y = MD$LIFEXP,
                  method = "pls",
                  tuneGrid = PLS_Grid,
                  trControl = KFold_Control)

##################################
# Reporting the cross-validation results
# for the PLS model
##################################
PLS_Tune
## Partial Least Squares 
## 
## 294 samples
##   5 predictor
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 266, 264, 265, 264, 265, 266, ... 
## Resampling results across tuning parameters:
## 
##   ncomp  RMSE      Rsquared   MAE     
##   1      5.107641  0.5809693  4.152802
##   2      3.229570  0.8398823  2.428972
##   3      2.819900  0.8781609  2.095819
##   4      2.769699  0.8837208  2.114378
##   5      2.768609  0.8838715  2.110610
## 
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was ncomp = 5.
PLS_Tune$finalModel
## Partial least squares regression , fitted with the orthogonal scores algorithm.
## Call:
## plsr(formula = .outcome ~ ., ncomp = ncomp, data = dat, method = "oscorespls")
(PLS_Tune_RMSE <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
                 c("RMSE")])
## [1] 2.768609
(PLS_Tune_Rsquared <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
                 c("Rsquared")])
## [1] 0.8838715
(PLS_Tune_MAE <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
                 c("MAE")])
## [1] 2.11061
##################################
# Identifying and plotting the
# best model predictors
# for the PLS model
##################################
PLS_VarImp <- varImp(PLS_Tune, scale = TRUE)
plot(PLS_VarImp, 
     scales=list(y=list(cex = .95)),
     main="Ranked Variable Importance : PLS",
     xlab="Scaled Variable Importance Metrics",
     ylab="Predictors",
     cex=2,
     origin=0,
     alpha=0.45)


1.3.6.5 Cubist Regression (CUBIST)


Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the CUBIST model
##################################
CUBIST_Grid = expand.grid(committees = c(10, 20, 30, 40, 50), 
                          neighbors = c(0, 3, 6, 9))


##################################
# Running the CUBIST model
# by setting the caret method to 'cubist'
##################################
set.seed(12345678)
CUBIST_Tune <- train(x = MD.Model.Predictors,
                   y = MD$LIFEXP,
                   method = "cubist",
                   tuneGrid = CUBIST_Grid,
                   trControl = KFold_Control)

##################################
# Reporting the cross-validation results
# for the CUBIST model
##################################
CUBIST_Tune
## Cubist 
## 
## 294 samples
##   5 predictor
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 266, 264, 265, 264, 265, 266, ... 
## Resampling results across tuning parameters:
## 
##   committees  neighbors  RMSE      Rsquared   MAE     
##   10          0          2.349910  0.9176282  1.743119
##   10          3          2.463103  0.9118159  1.826258
##   10          6          2.414374  0.9147585  1.779205
##   10          9          2.371853  0.9172327  1.751077
##   20          0          2.325278  0.9194823  1.729501
##   20          3          2.452245  0.9128512  1.817801
##   20          6          2.403467  0.9158193  1.775160
##   20          9          2.365302  0.9179693  1.750785
##   30          0          2.288992  0.9216589  1.703604
##   30          3          2.441377  0.9135986  1.809325
##   30          6          2.385727  0.9169876  1.763454
##   30          9          2.346537  0.9191819  1.738438
##   40          0          2.288357  0.9211136  1.706122
##   40          3          2.436769  0.9136943  1.811555
##   40          6          2.383490  0.9168822  1.766773
##   40          9          2.345004  0.9190023  1.743124
##   50          0          2.283377  0.9214369  1.704048
##   50          3          2.434991  0.9139000  1.815273
##   50          6          2.382253  0.9170399  1.769362
##   50          9          2.343006  0.9192188  1.745291
## 
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were committees = 50 and neighbors = 0.
CUBIST_Tune$finalModel
## 
## Call:
## cubist.default(x = x, y = y, committees = param$committees)
## 
## Number of samples: 294 
## Number of predictors: 5 
## 
## Number of committees: 50 
## Number of rules per committee: 6, 2, 4, 4, 5, 2, 4, 3, 4, 2, 4, 2, 3, 2, 4, 2, 3, 2, 4, 2 ...
(CUBIST_Tune_RMSE <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
                              CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
                 c("RMSE")])
## [1] 2.283377
(CUBIST_Tune_Rsquared <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
                              CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
                 c("Rsquared")])
## [1] 0.9214369
(CUBIST_Tune_MAE <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
                              CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
                 c("MAE")])
## [1] 1.704048
##################################
# Identifying and plotting the
# best model predictors
# for the CUBIST model
##################################
CUBIST_VarImp <- varImp(CUBIST_Tune, scale = TRUE)
plot(CUBIST_VarImp, 
     scales=list(y=list(cex = .95)),
     main="Ranked Variable Importance : CUBIST",
     xlab="Scaled Variable Importance Metrics",
     ylab="Predictors",
     cex=2,
     origin=0,
     alpha=0.45)

1.3.7 Model Performance Validation


Code Chunk | Output
##################################
# Evaluating the models
# on the model test data
##################################

##################################
# Formulating the DALEX object
# for the Best GBM model
##################################
GBM_DALEX <- DALEX::explain(GBM_Tune, 
                            data = MT.Model.Predictors, 
                            y = MT$LIFEXP,
                            verbose = FALSE,
                            label = "GBM")

(GBM_DALEX_Performance <- model_performance(GBM_DALEX))
## Measures for:  regression
## mse        : 3.493083 
## rmse       : 1.868979 
## r2         : 0.9362058 
## mad        : 0.9972678
## 
## Residuals:
##          0%         10%         20%         30%         40%         50% 
## -4.20752484 -2.15021430 -1.48672408 -0.87866008 -0.39263579 -0.01705431 
##         60%         70%         80%         90%        100% 
##  0.35387527  0.67211084  1.30280583  2.02235201  6.02771793
(GBM_DALEX_Diagnostics <- model_diagnostics(GBM_DALEX))
##     GENDER       INFMOR           PERCAP            CLTECH      
##  Male  :43   Min.   :0.5306   Min.   :-1.4775   Min.   :  0.20  
##  Female:29   1st Qu.:1.5623   1st Qu.: 0.8076   1st Qu.: 31.15  
##              Median :2.6602   Median : 1.5798   Median : 83.50  
##              Mean   :2.5206   Mean   : 1.7061   Mean   : 67.12  
##              3rd Qu.:3.4491   3rd Qu.: 2.8175   3rd Qu.:100.00  
##              Max.   :4.4864   Max.   : 4.2333   Max.   :100.00  
##      NCOMOR            y             y_hat         residuals       
##  Min.   :2.511   Min.   :53.79   Min.   :56.81   Min.   :-4.20753  
##  1st Qu.:3.887   1st Qu.:67.49   1st Qu.:67.37   1st Qu.:-1.04218  
##  Median :4.685   Median :73.35   Median :72.36   Median :-0.01705  
##  Mean   :4.736   Mean   :72.42   Mean   :72.48   Mean   :-0.06016  
##  3rd Qu.:5.631   3rd Qu.:78.39   3rd Qu.:78.86   3rd Qu.: 0.87535  
##  Max.   :7.579   Max.   :86.20   Max.   :84.55   Max.   : 6.02772  
##  abs_residuals        label                ids       
##  Min.   :0.06685   Length:72          Min.   : 1.00  
##  1st Qu.:0.46126   Class :character   1st Qu.:18.75  
##  Median :0.99727   Mode  :character   Median :36.50  
##  Mean   :1.40941                      Mean   :36.50  
##  3rd Qu.:1.99579                      3rd Qu.:54.25  
##  Max.   :6.02772                      Max.   :72.00
plot(GBM_DALEX_Diagnostics, 
     variable = "y", 
     yvariable = "y_hat") +
  geom_point(size=3) +
  scale_x_continuous("Observed LIFEXP") + 
  scale_y_continuous("Predicted LIFEXP") + 
  geom_abline(slope = 1) + 
  ggtitle("GBM: Observed and Predicted LIFEXP")

##################################
# Formulating the DALEX object
# for the Best RF model
##################################
RF_DALEX <- DALEX::explain(RF_Tune, 
                           data = MT.Model.Predictors, 
                           y = MT$LIFEXP,
                           verbose = FALSE,
                           label = "RF")

(RF_DALEX_Performance <- model_performance(RF_DALEX))
## Measures for:  regression
## mse        : 4.504021 
## rmse       : 2.122268 
## r2         : 0.917743 
## mad        : 1.284074
## 
## Residuals:
##          0%         10%         20%         30%         40%         50% 
## -5.22877943 -2.17233443 -1.33595313 -0.66251307 -0.31119611  0.01921082 
##         60%         70%         80%         90%        100% 
##  0.26151554  0.80194556  1.84018856  2.72166332  6.62793907
(RF_DALEX_Diagnostics <- model_diagnostics(RF_DALEX))
##     GENDER       INFMOR           PERCAP            CLTECH      
##  Male  :43   Min.   :0.5306   Min.   :-1.4775   Min.   :  0.20  
##  Female:29   1st Qu.:1.5623   1st Qu.: 0.8076   1st Qu.: 31.15  
##              Median :2.6602   Median : 1.5798   Median : 83.50  
##              Mean   :2.5206   Mean   : 1.7061   Mean   : 67.12  
##              3rd Qu.:3.4491   3rd Qu.: 2.8175   3rd Qu.:100.00  
##              Max.   :4.4864   Max.   : 4.2333   Max.   :100.00  
##      NCOMOR            y             y_hat         residuals       
##  Min.   :2.511   Min.   :53.79   Min.   :54.37   Min.   :-5.22878  
##  1st Qu.:3.887   1st Qu.:67.49   1st Qu.:68.13   1st Qu.:-1.20707  
##  Median :4.685   Median :73.35   Median :72.39   Median : 0.01921  
##  Mean   :4.736   Mean   :72.42   Mean   :72.31   Mean   : 0.11035  
##  3rd Qu.:5.631   3rd Qu.:78.39   3rd Qu.:78.82   3rd Qu.: 1.46992  
##  Max.   :7.579   Max.   :86.20   Max.   :84.60   Max.   : 6.62794  
##  abs_residuals        label                ids       
##  Min.   :0.03082   Length:72          Min.   : 1.00  
##  1st Qu.:0.46750   Class :character   1st Qu.:18.75  
##  Median :1.28407   Mode  :character   Median :36.50  
##  Mean   :1.56501                      Mean   :36.50  
##  3rd Qu.:2.23392                      3rd Qu.:54.25  
##  Max.   :6.62794                      Max.   :72.00
plot(RF_DALEX_Diagnostics, 
     variable = "y", 
     yvariable = "y_hat") +
  geom_point(size=3) +
  scale_x_continuous("Observed LIFEXP") + 
  scale_y_continuous("Predicted LIFEXP") + 
  geom_abline(slope = 1) + 
  ggtitle("RF: Observed and Predicted LIFEXP")

##################################
# Formulating the DALEX object
# for the Best NN model
##################################
NN_DALEX <- DALEX::explain(NN_Tune, 
                           data = MT.Model.Predictors, 
                           y = MT$LIFEXP,
                           verbose = FALSE,
                           label = "NN")

(NN_DALEX_Performance <- model_performance(NN_DALEX))
## Measures for:  regression
## mse        : 3.981273 
## rmse       : 1.995313 
## r2         : 0.92729 
## mad        : 1.331898
## 
## Residuals:
##         0%        10%        20%        30%        40%        50%        60% 
## -5.0566398 -1.9253734 -1.4168918 -1.1691032 -0.3808666  0.2508203  0.3866501 
##        70%        80%        90%       100% 
##  1.0424427  1.7914332  2.9384548  4.8353156
(NN_DALEX_Diagnostics <- model_diagnostics(NN_DALEX))
##     GENDER       INFMOR           PERCAP            CLTECH      
##  Male  :43   Min.   :0.5306   Min.   :-1.4775   Min.   :  0.20  
##  Female:29   1st Qu.:1.5623   1st Qu.: 0.8076   1st Qu.: 31.15  
##              Median :2.6602   Median : 1.5798   Median : 83.50  
##              Mean   :2.5206   Mean   : 1.7061   Mean   : 67.12  
##              3rd Qu.:3.4491   3rd Qu.: 2.8175   3rd Qu.:100.00  
##              Max.   :4.4864   Max.   : 4.2333   Max.   :100.00  
##      NCOMOR            y             y_hat         residuals      
##  Min.   :2.511   Min.   :53.79   Min.   :53.77   Min.   :-5.0566  
##  1st Qu.:3.887   1st Qu.:67.49   1st Qu.:67.91   1st Qu.:-1.3453  
##  Median :4.685   Median :73.35   Median :72.39   Median : 0.2508  
##  Mean   :4.736   Mean   :72.42   Mean   :72.30   Mean   : 0.1240  
##  3rd Qu.:5.631   3rd Qu.:78.39   3rd Qu.:78.90   3rd Qu.: 1.2255  
##  Max.   :7.579   Max.   :86.20   Max.   :84.31   Max.   : 4.8353  
##  abs_residuals        label                ids       
##  Min.   :0.08507   Length:72          Min.   : 1.00  
##  1st Qu.:0.68395   Class :character   1st Qu.:18.75  
##  Median :1.33190   Mode  :character   Median :36.50  
##  Mean   :1.58876                      Mean   :36.50  
##  3rd Qu.:2.14093                      3rd Qu.:54.25  
##  Max.   :5.05664                      Max.   :72.00
plot(NN_DALEX_Diagnostics, 
     variable = "y", 
     yvariable = "y_hat") +
  geom_point(size=3) +
  scale_x_continuous("Observed LIFEXP") + 
  scale_y_continuous("Predicted LIFEXP") + 
  geom_abline(slope = 1) + 
  ggtitle("NN: Observed and Predicted LIFEXP")

##################################
# Formulating the DALEX object
# for the Best PLS model
##################################
PLS_DALEX <- DALEX::explain(PLS_Tune,
                            data = MT.Model.Predictors,
                            y = MT$LIFEXP,
                            verbose = FALSE,
                            label = "PLS")

(PLS_DALEX_Performance <- model_performance(PLS_DALEX))
## Measures for:  regression
## mse        : 8.772658 
## rmse       : 2.961867 
## r2         : 0.8397849 
## mad        : 1.687233
## 
## Residuals:
##         0%        10%        20%        30%        40%        50%        60% 
## -8.2953487 -3.3619601 -2.3734314 -1.1325844 -0.4814388  0.1923809  0.7761085 
##        70%        80%        90%       100% 
##  1.3418691  2.3002494  4.1779864  6.2664318
(PLS_DALEX_Diagnostics <- model_diagnostics(PLS_DALEX))
##     GENDER       INFMOR           PERCAP            CLTECH      
##  Male  :43   Min.   :0.5306   Min.   :-1.4775   Min.   :  0.20  
##  Female:29   1st Qu.:1.5623   1st Qu.: 0.8076   1st Qu.: 31.15  
##              Median :2.6602   Median : 1.5798   Median : 83.50  
##              Mean   :2.5206   Mean   : 1.7061   Mean   : 67.12  
##              3rd Qu.:3.4491   3rd Qu.: 2.8175   3rd Qu.:100.00  
##              Max.   :4.4864   Max.   : 4.2333   Max.   :100.00  
##      NCOMOR            y             y_hat         residuals       
##  Min.   :2.511   Min.   :53.79   Min.   :58.14   Min.   :-8.29535  
##  1st Qu.:3.887   1st Qu.:67.49   1st Qu.:67.39   1st Qu.:-1.73571  
##  Median :4.685   Median :73.35   Median :72.32   Median : 0.19238  
##  Mean   :4.736   Mean   :72.42   Mean   :72.34   Mean   : 0.07797  
##  3rd Qu.:5.631   3rd Qu.:78.39   3rd Qu.:78.60   3rd Qu.: 1.59971  
##  Max.   :7.579   Max.   :86.20   Max.   :86.20   Max.   : 6.26643  
##  abs_residuals        label                ids       
##  Min.   :0.01028   Length:72          Min.   : 1.00  
##  1st Qu.:0.77705   Class :character   1st Qu.:18.75  
##  Median :1.68723   Mode  :character   Median :36.50  
##  Mean   :2.27256                      Mean   :36.50  
##  3rd Qu.:3.40953                      3rd Qu.:54.25  
##  Max.   :8.29535                      Max.   :72.00
plot(PLS_DALEX_Diagnostics, 
     variable = "y", 
     yvariable = "y_hat") +
  geom_point(size=3) +
  scale_x_continuous("Observed LIFEXP") + 
  scale_y_continuous("Predicted LIFEXP") + 
  geom_abline(slope = 1) + 
  ggtitle("PLS: Observed and Predicted LIFEXP")

##################################
# Formulating the DALEX object
# for the Best CUBIST model
##################################
CUBIST_DALEX <- DALEX::explain(CUBIST_Tune,
                            data = MT.Model.Predictors,
                            y = MT$LIFEXP,
                            verbose = FALSE,
                            label = "CUBIST")

(CUBIST_DALEX_Performance <- model_performance(CUBIST_DALEX))
## Measures for:  regression
## mse        : 4.252961 
## rmse       : 2.062271 
## r2         : 0.9223281 
## mad        : 1.504065
## 
## Residuals:
##          0%         10%         20%         30%         40%         50% 
## -4.24762244 -2.06826235 -1.58225898 -1.15000147 -0.43114893  0.03237576 
##         60%         70%         80%         90%        100% 
##  0.34857635  1.11785478  1.58806246  2.95097520  6.41232629
(CUBIST_DALEX_Diagnostics <- model_diagnostics(CUBIST_DALEX))
##     GENDER       INFMOR           PERCAP            CLTECH      
##  Male  :43   Min.   :0.5306   Min.   :-1.4775   Min.   :  0.20  
##  Female:29   1st Qu.:1.5623   1st Qu.: 0.8076   1st Qu.: 31.15  
##              Median :2.6602   Median : 1.5798   Median : 83.50  
##              Mean   :2.5206   Mean   : 1.7061   Mean   : 67.12  
##              3rd Qu.:3.4491   3rd Qu.: 2.8175   3rd Qu.:100.00  
##              Max.   :4.4864   Max.   : 4.2333   Max.   :100.00  
##      NCOMOR            y             y_hat         residuals       
##  Min.   :2.511   Min.   :53.79   Min.   :55.96   Min.   :-4.24762  
##  1st Qu.:3.887   1st Qu.:67.49   1st Qu.:67.43   1st Qu.:-1.51031  
##  Median :4.685   Median :73.35   Median :72.64   Median : 0.03238  
##  Mean   :4.736   Mean   :72.42   Mean   :72.32   Mean   : 0.10287  
##  3rd Qu.:5.631   3rd Qu.:78.39   3rd Qu.:78.58   3rd Qu.: 1.29561  
##  Max.   :7.579   Max.   :86.20   Max.   :84.74   Max.   : 6.41233  
##  abs_residuals         label                ids       
##  Min.   :0.009592   Length:72          Min.   : 1.00  
##  1st Qu.:0.499008   Class :character   1st Qu.:18.75  
##  Median :1.504065   Mode  :character   Median :36.50  
##  Mean   :1.591015                      Mean   :36.50  
##  3rd Qu.:1.926649                      3rd Qu.:54.25  
##  Max.   :6.412326                      Max.   :72.00
plot(CUBIST_DALEX_Diagnostics, 
     variable = "y", 
     yvariable = "y_hat") +
  geom_point(size=3) +
  scale_x_continuous("Observed LIFEXP") + 
  scale_y_continuous("Predicted LIFEXP") + 
  geom_abline(slope = 1) + 
  ggtitle("CUBIST: Observed and Predicted LIFEXP")

1.3.8 Model Selection


Code Chunk | Output
##################################
# Consolidating the performance
# on the model test data
##################################
plot(GBM_DALEX_Performance,
     RF_DALEX_Performance,
     NN_DALEX_Performance,
     PLS_DALEX_Performance,
     CUBIST_DALEX_Performance)

plot(GBM_DALEX_Performance,
     RF_DALEX_Performance,
     NN_DALEX_Performance,
     PLS_DALEX_Performance,
     CUBIST_DALEX_Performance,
     geom = "boxplot")

plot(GBM_DALEX_Performance,
     RF_DALEX_Performance,
     NN_DALEX_Performance,
     PLS_DALEX_Performance,
     CUBIST_DALEX_Performance,
     geom = "histogram")

##################################
# Consolidating the variable importance
# on the model test data
##################################
GBM_DALEX_VariableImportance    <- model_parts(GBM_DALEX,
                                               loss_function = loss_root_mean_square,
                                               B = 200, 
                                               N = NULL)
RF_DALEX_VariableImportance     <- model_parts(RF_DALEX,
                                               loss_function = loss_root_mean_square,
                                               B = 200, 
                                               N = NULL)
NN_DALEX_VariableImportance     <- model_parts(NN_DALEX,
                                               loss_function = loss_root_mean_square,
                                               B = 200, 
                                               N = NULL)
PLS_DALEX_VariableImportance    <- model_parts(PLS_DALEX,
                                               loss_function = loss_root_mean_square,
                                               B = 200, 
                                               N = NULL)
CUBIST_DALEX_VariableImportance <- model_parts(CUBIST_DALEX,
                                               loss_function = loss_root_mean_square,
                                               B = 200, 
                                               N = NULL)

plot(GBM_DALEX_VariableImportance, 
     RF_DALEX_VariableImportance, 
     NN_DALEX_VariableImportance,
     PLS_DALEX_VariableImportance,
     CUBIST_DALEX_VariableImportance)

1.3.9 Model Post-Hoc Analysis


Code Chunk | Output
##################################
# Summarizing the variable importance
# for the final model - GBM
##################################
GBM_DALEX_VariableImportance
##       variable mean_dropout_loss label
## 1 _full_model_          1.868979   GBM
## 2       PERCAP          2.088901   GBM
## 3       GENDER          2.237822   GBM
## 4       CLTECH          2.334086   GBM
## 5       NCOMOR          4.002445   GBM
## 6       INFMOR          7.689843   GBM
## 7   _baseline_         10.077581   GBM
plot(GBM_DALEX_VariableImportance)

##################################
# Formulating the partial dependence plots
# for the final model - GBM
# using the different variables
##################################
GBM_DALEX_PartialDependencePlot_INFMOR <- model_profile(GBM_DALEX, 
                                                        variables = "INFMOR")
GBM_DALEX_PartialDependencePlot_NCOMOR <- model_profile(GBM_DALEX, 
                                                        variables = "NCOMOR")
GBM_DALEX_PartialDependencePlot_CLTECH <- model_profile(GBM_DALEX, 
                                                        variables = "CLTECH")
GBM_DALEX_PartialDependencePlot_PERCAP <- model_profile(GBM_DALEX, 
                                                        variables = "PERCAP")

(GBM_DALEX_PDP_INFMOR <- plot(GBM_DALEX_PartialDependencePlot_INFMOR, 
                              geom = "profiles"))

(GBM_DALEX_PDP_NCOMOR <- plot(GBM_DALEX_PartialDependencePlot_NCOMOR, 
                              geom = "profiles"))

(GBM_DALEX_PDP_CLTECH <- plot(GBM_DALEX_PartialDependencePlot_CLTECH, 
                              geom = "profiles"))

(GBM_DALEX_PDP_PERCAP <- plot(GBM_DALEX_PartialDependencePlot_PERCAP, 
                              geom = "profiles"))

##################################
# Formulating the grouped partial dependence plots
# for the final model - GBM
# using the different variables
##################################
GBM_DALEX_GroupedPartialDependencePlot_INFMOR <- model_profile(GBM_DALEX, 
                                                               variables = "INFMOR",
                                                               groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_NCOMOR <- model_profile(GBM_DALEX, 
                                                               variables = "NCOMOR",
                                                               groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_CLTECH <- model_profile(GBM_DALEX, 
                                                               variables = "CLTECH",
                                                               groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_PERCAP <- model_profile(GBM_DALEX, 
                                                               variables = "PERCAP",
                                                               groups = "GENDER")

(GBM_DALEX_GPDP_INFMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_INFMOR, 
                              geom = "profiles"))

(GBM_DALEX_GPDP_NCOMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_NCOMOR, 
                              geom = "profiles"))

(GBM_DALEX_GPDP_CLTECH <- plot(GBM_DALEX_GroupedPartialDependencePlot_CLTECH, 
                              geom = "profiles"))

(GBM_DALEX_GPDP_PERCAP <- plot(GBM_DALEX_GroupedPartialDependencePlot_PERCAP, 
                              geom = "profiles"))

1.4 Summary

2. References


[Book] Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models With examples in R and Python by Przemyslaw Biecek and Tomasz Burzykowski
[Book] Explainable Machine Learning: A Guide for Making Black Box Models Explainable by Christoph Molnar
[Book] Explainable AI: Interpreting, Explaining and Visualizing Deep Learning by Wojciech Samek, Gregoire Montavon, Andrea Vedaldi, Lars Kai Hansen and Klaus-Robert Muller
[R Package] DALEX by Przemyslaw Biecek, Szymon Maksymiuk and Hubert Baniecki
[R Package] iml by Christoph Molnar
[R Package] ALEPlot by Dan Apley
[R Package] randomForest by Leo Breiman, Adele Cutler, Andy Liaw and Matthew Wiener
[R Package] auditor by Alicja Gosiewska, Przemyslaw Biecek, Hubert Baniecki and Tomasz Mikołajczyk
[R Package] fastshap by Brandon Greenwell
[R Package] rms by Frank Harrell
[R Package] EIX by Szymon Maksymiuk, Ewelina Karbowiak and Przemyslaw Biecek
[R Package] parsnip by Max Kuhn and Davis Vaughan
[R Package] h2o by Tomas Fryda, Erin LeDell, Navdeep Gill, Spencer Aiello, Anqi Fu, Arno Candel, Cliff Click, Tom Kraljevic, Tomas Nykodym, Patrick Aboyoun, Michal Kurka, Michal Malohlava, Sebastien Poirier and Wendy Wong
[R Package] tidymodels by Max Kuhn and Hadley Wickham
[R Package] e1071 by David Meyer, Evgenia Dimitriadou, Kurt Hornik, Andreas Weingessel and Friedrich Leisch
[R Package] lime by Emil Hvitfeldt, Thomas Lin Pedersen and Michael Benesty
[R Package] ExplainPrediction by Marko Robnik-Sikonja
[R Package] localModel by Przemyslaw Biecek and Mateusz Staniak
[R Package] skimr by Elin Waring
[R Package] corrplot by Taiyun Wei
[R Package] lares by Bernardo Lares
[R Package] minerva by Michele Filosi
[R Package] CORElearn by Marko Robnik-Sikonja and Petr Savicky
[R Package] caret by Max Kuhn
[R Package] gbm by Brandon Greenwell, Bradley Boehmke, Jay Cunningham and GBM Developers
[R Package] randomForest by Andy Liaw
[R Package] nnet by Brian Ripley
[R Package] pls by Kristian Hovde Liland
[R Package] Cubist by Max Kuhn
[R Package] patchwork by Thomas Lin Pedersen
[Article] Interpretation Methods for Black-Box Machine Learning Models in Insurance Rating-Type Applications by Gabe Taylor, Sunish Menon, Huimin Ru, Ray Wright, Xin Hunt and Ralph Abbey
[Article] 4 Model-Agnostic Interpretability Techniques for Complex Models by Funda Gunes
[Article] How Can We Provide Post-Hoc Explanations for Black-Box AI Models? by Joy Lin
[Publication] A Survey of Methods for Explaining Black Box Models by Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti and Dino Pedreschi (ACM Computing Surveys)
[Publication] iml: An R package for Interpretable Machine Learning by Christoph Molnar (Journal of Open Source Software)